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[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please kindly refer the the weaknesses." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The motivation is interesting and well-justified. The hierarchical structure of biological data presents significant challenges for distinguishing species and identifying evolutionary traits.\n2. The techniques employed in HComP-Net appear technically feasible. The use of contrastive loss for learning clustered features has proven effective in self-supervised learning, while orthogonality loss helps capture diverse features.\n3. The visualization results are clear and impressive.\n4. The paper is well-structured and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper applies deep learning techniques to uncover evolutionary traits in biological data. It leverages contrastive and orthogonality losses to facilitate hierarchical prototype learning. Additionally, the paper introduces over-specificity and discriminative losses to guide and constrain model training. The proposed method demonstrates improved performance over baseline methods across multiple benchmark datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. It seems that the over-specificity and discriminative losses play opposing roles in the direction of model optimization, which raises the question of whether these two losses might interact and lead to abnormal model convergence. It would be beneficial if the authors could provide some theoretical or experimental analysis on this issue.\n2. This is a minor point, but the overall method appears to be a combination of multiple techniques, making the flowchart somewhat complex and redundant." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see the weaknesses." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This paper investigates a highly intriguing task: identifying visual features preserved during species evolution. I believe this task is inherently challenging due to the limited and often insufficient quality of training data, making it difficult to obtain stable, semantically interpretable visual features. In this work, the design of the loss function and the associated explanations are intuitive and easy to understand." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The work a novel framework called Hierarchy aligned Commonality through Prototypical Networks (HComP-Net) aimed at discovering evolutionary traits among species by learning hierarchical prototypes over the tree of life. It addresses the challenges of existing prototype-based methods that often produce over-specific prototypes at internal nodes, which can hinder the identification of common traits shared by descendant species. HComP-Net employs a unique over-specificity loss, a discriminative loss to ensure prototypes are absent in contrasting species, and a masking module to maintain classification performance. Through empirical analysis on various datasets, including birds, fishes, turtles, and butterflies, the authors demonstrate that HComP-Net effectively learns accurate, semantically consistent, and generalizable prototypes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The first comment is related to the choice of using visual data to identify common features in species evolution. Given the scarcity of high-quality biological images, especially in the context of vast evolutionary networks, and the inherent issues of imbalance and interference in such images, I wonder if it might be more precise to analyze common traits directly from textual descriptions or anatomical data. Could you please elaborate on the rationale behind prioritizing visual data for this task?\n\n2. The paper introduces several loss functions aimed at ensuring the diversity and effectiveness of the learned prototypes. I would be very interested to see ablation studies on these loss functions to better understand their individual impact.\n\n3. In Figure 4, when comparing the part consistency between HComP-Net and HPNet, different bird images are used. I am curious to know if this choice is justified and, if so, what the reasoning behind it is. Would it not be more appropriate to use the same images for a clearer comparison?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- The paper discusses many similarities with ProtoPNet and refers to it often, e.g. “For HPnet, we used the same hyperparameter settings and training strategy as used by ProtoPNet for the CUB-200-2011 dataset”, “We follow the same training strategy as provided by ProtoPNet for the CUB-200-2011 dataset.”. Why ProtoPNet is not used in the comparative experiments (also other non-hierarchical models are used there)?\n- How can the paper help to “advance our understanding of evolution”? E.g. There is a high chance that for the common species (whose pictures are available in large-scale datasets) the same features that were already used to make such a classification will be highlighted. Could the authors present a use case, in which the solution could be successfully used in practice to contribute to the understanding of evolution?\n- How are the representative images (also could be perceived as prototypical images) chosen from the dataset to allow for proper explanations (some images can be taken e.g. from a non-typical angle and do not show the prototypical features well and make the explanations difficult)?\n- Is it possible (and if yes, how) to use this solution in other hierarchical problems (not in the area of biology)?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The idea presented in the paper is interesting and original. The idea is quite simple (which is a plus). I liked that the authors aimed to present a method that can help to facilitate the analysis of different species in biology. \n- The paper is well-written, and also presents a lot of nice and clean graphics and examples that make the content understandable." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a new framework that can be used for learning biological prototypes within hierarchies while avoiding the learning\nof over-specific features at internal nodes of the genetic tree. The authors perform tests with different datasets including mostly the pictures of birds, fishes and butterflies. The authors focus on the quantitative and qulitative evaluation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The motivation of the paper says that there are many image datasets in the biology, so ML can be used to provide some new visual suggestions for common traits in species belonging to a common group. Nevertheless, such suggestions seem to be more important for some newly discovered species (and not the ones that are already well-known), and for such species there is a possibility of not having so many images. This can decrease the practicality of the method. Statements such as “Furthermore, HComP-Net demonstrates a unique ability to generate novel hypotheses about evolutionary traits, showcasing its potential in advancing our understanding of evolution” are too bold in my opinion.\n- Another issue with a possible practical use of the method is that the proposed solution to provide semantically meaningful information requires human annotation, which can be very subjective. The authors mention this limitation in the appendix, however they do not give any solution for a mitigation. \n- minor: some typos/grammatical mistakes can be found in the paper, e.g. “that are not just shared across all it descendant species but are also” -> “that are not just shared across all ITS descendant species but are also”" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "NA" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1) The CNN backbone used was ConvNeXt-tiny architecture; why the fine-grained accuracy from this architecture is not included in Table 1? Will it be better/worse when compared to other methods on the Table?\n\n2) In terms of semantically meaningful prototypes, could you discuss the possibility of obtaining similar findings using Explainable AI techniques?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Originality: The research builds on the problem initially formulated in HPNet (Haze et al., 2019), specifically addressing the challenge of over-specific prototypes. The authors introduced over-specificity and discriminative loss functions, enabling HComP-Net to learn prototypes that adhere to the hierarchical structure of a phylogenetic tree. This approach enhances interpretability and brings a fresh perspective to prototype learning.\n\nQuality: Given the identified problem of over-specific prototypes, the authors demonstrate the efficacy of their methods across fine-grained classification tasks, also showing advancements in interpretability. The proposed model performs consistently well in these tasks, validating its quality and effectiveness.\n\nClarity: The paper is well-organized, with clear explanations of the background, literature, methodology, experimental setup, and results. This clarity enhances the readability and accessibility of the research, making its contributions understandable and well-supported.\n\nSignificance: This work highlights the potential of interpretable representation learning driven by structured hierarchical knowledge. By using a phylogenetic tree for guidance, the model provides insights into trait evolution and aligns closely with the biology-inspired data structure, marking a contribution at the intersection of AI and biology." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work presents an extended approach for hierarchical prototype learning by training a neural network to discover hierarchical prototypes using structured information from a phylogenetic tree. Building upon HPNet (Haze et al., 2019), this approach, termed HComP-Net, contrasts with traditional models that use flat structures by incorporating loss functions such as over-specificity loss and discriminative loss. These functions enable HComP-Net to learn prototypes that align with the hierarchical structure of the tree, enhancing interpretability and consistency. Empirical results highlight HComP-Net’s ability to produce accurate, semantically coherent prototypes transferable to unseen species. Tested on a dataset of 190 bird species and additional organisms, the model performs better than baseline models. Additionally, HComP-Net has been shown to generate visual hypotheses on evolutionary traits, offering insight into traits across various levels of the phylogenetic tree. This research underscores the potential of interpretable representation learning using structured hierarchical prior knowledge." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) On the Need for a Separate Masking Module\nThe fact that an additional masking module is required suggests that the initial contributions, particularly the over-specificity and discriminative loss functions, may not fully address the issue of over-specific prototypes. This need points to a limitation in the proposed loss functions' effectiveness in preventing prototypes from becoming overly tailored to specific species. Ideally, a more robust solution would directly manage prototype specificity through the loss functions alone, reducing reliance on extra modules that could complicate the model and potentially impact interpretability or scalability.\n\n2) On the Role of the Phylogenetic Tree in Classification\nIn section 5.1, the authors suggest that achieving high classification accuracy is not the primary goal. However, providing the model with additional information, like a phylogenetic tree during training and inference, could indeed aid classification performance. The tree may allow the model to leverage hierarchical relationships, which could enhance classification accuracy by using shared traits among related species. Thus, there seems to be a disconnect between the claim of not prioritizing classification accuracy and the model's design, which inherently includes information that could enhance it." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Why does the over-specificity loss adopt a specific log-tanh loss form? Doesn’t it simply establish an arbitrary criterion for identifying overly specific prototypes? This choice requires further explanation and discussion." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The method details are well-demonstrated, with a clear overview and examples that make the content easy to follow.\n\nThe study presents a novel approach to improving the interpretability of prototypical networks, allowing for more accurate representation of parent-child relationships.\n\nIt enhances the specialization of deep networks applied in scientific discovery, particularly in phylogenetic analysis." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses a scientific problem: discovering evolutionary traits in biology from images. To tackle this, it proposes the Hierarchy-Aligned Commonality through Prototypical Network (HComP-Net). The primary objective is to reduce over-specific prototypes that lose common features expected to be observed in all species sharing a common ancestor. To achieve this, the approach applies contrastive, orthogonality, and discriminative losses to the prototypes, and introduces over-specificity loss and masking to mitigate over-specific prototypes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Two limits of experimental demonstration. \n1. The primary contribution lies in incorporating parent-child relationships to reduce over-specific prototypes within the contrastive losses, while the architecture does not appear to include specific structures for establishing a prototype hierarchy.\nIn my understanding, proposing a hierarchical structure is a contribution of HPNet, not HCompPNet. This point should be clarified in title and method description. \n2. The exclusion of certain related works leaves me a question about novelty on practical impact. For instance, PIPNet, a recent and closely related method employing self-supervised learning (2023), is not included in the comparison. It only uses HPNet from 2019. However, semantic gaps has relevance to over-specificity, and authors also mention strong motivation from PIPNet. The reason why the network is excluded needs more detailed explanation or comparison in experiments." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024what,\ntitle={What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4sDicVEy6M},\nnote={under review}\n}" }, "abstract": { "value": "A grand challenge in biology is to discover evolutionary traits---features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as phylogenetic tree). With the growing availability of image repositories in biology, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes. However, current prototype-based methods are mostly designed to operate over a flat structure of classes and face several challenges in discovering hierarchical prototypes, including the issue of learning over-specific prototypes at internal nodes. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net). The key novelties in HComP-Net include a novel over-specificity loss to avoid learning over-specific prototypes, a novel discriminative loss to ensure prototypes at an internal node are absent in the contrasting set of species with different ancestry, and a novel masking module to allow for the exclusion of over-specific prototypes at higher levels of the tree without hampering classification performance. We empirically show that HComP-Net learns prototypes that are accurate, semantically consistent, and generalizable to unseen species in comparison to baselines." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "deep learning", "interpretability", "prototype-based neural network", "phylogeny", "computer vision" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/9c41d5e1c49689ab4c83247c9ad9d777f3a49966.pdf" }, "presentation": null, "primary_area": { "value": "applications to physical sciences (physics, chemistry, biology, etc.)" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. What's the training loss of the GIMF?\n\n2. Can GIMF obtain a Pareto set of solutions?\n\n3. I noticed the authors used multi-modal fusion, but I don't quite understand this part. Does it mean that each subproblem requires training a single model and then performing model fusion at the end?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The writing is good.\n\n2. The experiments are detailed, and the results are competitive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to fully leverage the intrinsic features of problem instances by proposing a novel graph-image multimodal fusion framework for solving multi-objective combinatorial optimization (MOCO). The authors introduce the concept of \"image\" for MOCO to better capture the spatial structure of problem instances, enhancing the learning process. They also propose a problem-size adaptive resolution strategy to improve generalization. Finally, the paper presents a multimodal fusion mechanism with modality-specific bottlenecks to efficiently integrate graph and image information." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. I believe the role of the image concept in CO is questionable. To some extent, using images in CO results in information loss and requires more space to represent. As far as I know, many Euclidean TSP models, like [1, 2], use positions directly as input, which requires less space and provides more precise information.\n\n2. Compared to typical neural MOCO methods, GIMF uses sparse matrix images as input, resulting in larger neural network sizes and an inability to handle larger-scale routing problems." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "In Table 1 and Table 2, the reported times of GIMF-P and GIMF-C are sometimes smaller than PMOCO and CHN, what is the reason for this phenomenon?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The major contribution of this paper is its integration of both graph and image modalities to enhance the representation learning for MOCO problems. The construction of coordinate images and the use of PSAR strategy are innovative steps that address the limitations of relying solely on graph information. The proposed MSB in multimodal fusion mechanism is also a novel contribution.\n\n2. The paper is well-organized and written in a clear and concise manner. The introduction effectively sets the stage by outlining the challenges in MOCO and the motivation behind the GIMF framework. The preliminary section clearly describes the definition of the MOCO problem and related concepts, as well as the graph transformer for MOCO. The methodology section is detailed, providing a clear explanation of the image construction process, the PSAR strategy, and the multimodal fusion mechanism.\n\n3. The significance mainly comes from its novelty.Specifically, leveraging a multimodal approach that incorporates image-modal information, which has the potential to improve many existing neural MOCO methods. The paper is also likely to inspire further research in constructing and learning from images of MOCO problems.\n\n4. The experimental results suggest that GIMF does not obviously increase computational time of the neural MOCO basebone. The major innovations PSAR and MSB are validated by ablation study." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a novel graph-image multimodal fusion (GIMF) framework that aims to enhance neural multi-objective combinatorial optimization (MOCO) methods. The GIMF framework integrates graph and image information of problem instances, which is designed to overcome the limitations of existing neural MOCO methods that rely solely on graph-modal information. The main contribution of the proposed method is the coordinate image construction, which provides complementary information to the graph representation.To improve the model's generalization across different problem sizes, a Problem-size Adaptive Resolution (PSAR) strategy is proposed during the image construction process, which helps maintain a stable density for both the image and patches. A multimodal fusion mechanism with Modality-Specific Bottlenecks (MSB) is designed to efficiently couple graph and image information. \n\nThe GIMF framework is implemented with two state-of-the-art neural MOCO backbones, namely CNH and PMOCO. Experimental results on classic MOCO problems demonstrate that GIMF can improve neural MOCO methods by providing image-modal information and exhibits superior generalization capability." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The proposed method could improve the performance of CNH and PMOCO, as well as their augment variants. However, sometimes the improvement seems marginal. In Table 1 and Table 2, the reported improvements are all mostly less than 0.001, and sometimes are as small as 0.0001. Meanwhile, the reported best results can not significantly outperform SOTA baselines." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Is this analogous to a game played on a chessboard? Are the authors providing definitions and citations related to reinforcement learning? Are you aiming to transform the graph problem into a gameplay problem on a chessboard?\n- Has reinforcement learning for MOCO in chessboard games been well studied elsewhere? If MOCO, such as TSP, is defined within the context of a chessboard game, isn’t it relatively straightforward? Does this require a graph modality, or can the image modality defined by the authors alone be sufficient to address the MOCO problems?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The main novelty of this paper lies in defining the image modality alongside the graph modality, offering a new perspective for addressing challenges in MOCO.\n- This approach enhances conventional heuristic algorithms by leveraging the combined information from both modalities." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a novel Graph-Image Multimodal Fusion (GIMF) framework designed to enhance multi-objective combinatorial optimization (MOCO) methods. By integrating both graph and image information from problem instances, the framework effectively addresses the limitations associated with relying solely on graph-modal information, particularly in the context of bi- and tri-objective traveling salesman problems (TSP)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- If the image modality has been defined, why still continue to use the graph modality? How could one approach solving an MOCO problem using only the image modality? Additionally, could you provide ablation studies to support this?\n- Since the graph can also be viewed as a transition matrix, what is the relationship between reinforcement learning and the authors' algorithm?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1. What does \"$\\bm{\\pi_i}$\" represent in the formula for calculating $\\nabla\\mathcal{L}(\\bm{\\theta})$ on line 136, or should it be changed to \"$\\pi_i$\"?\n\n2. What is the meaning of the line from $\\pi_t$ to $h_c$ in Figure 2? The authors should supplement the relationship between $\\pi_t$ and $h_c$ in the main text.\n\n3. Why choose the dimension of patch as $w=h=16$? The authors should provide an explanation or conduct ablation experiments." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tThis paper is well-organized and clear writing.\n2.\tThe proposed method demonstrates novelty.\n3.\tExperiments show that GIMF performs better on classic MOCO problems." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a generic graph-image multimodal fusion (GIMF) framework that integrates graph and image information of the problem instances to enhance neural MOCO. The framework consists of three main components: (1) a constructed coordinate image (2) a problem-size adaptive resolution strategy and (3) a multimodal fusion mechanism. Experimental results demonstrate its effectiveness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Some details and parameter settings were not explained clearly (see Questions below)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "N/A." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "S1. The GIMF framework successfully combines graph and image information, enriching representation learning for MOCO problems. \n\nS2. The PSAR strategy and modality-specific bottlenecks for multimodal fusion are well-justified and empirically validated.\n\nS3. Extensive experiments are conducted." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel approach for MOCO through graph-image multimodal fusion. The framework incorporates a constructed coordinate image and efficient multimodal fusion. Experimental results on MOCO problems show the advance of the proposed GIMF." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "W1. The improvement achieved by the proposed methods appears to be marginal.\n\nW2. The computational cost of the proposed GIMF framework seems considerably higher than state-of-the-art methods like EMNH. It seems that the performance gains come from a cost of efficiency. \n\nW3. Constructing images seems relatively straightforward for TSP problems, but how does this approach generalize to other real-world scenarios? For some tasks, image construction may be challenging—how do the authors envision addressing this limitation?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This paper proposes a graph-image multimodal fusion framework for neural multi-objective combinatorial optimization." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024neural,\ntitle={Neural Multi-Objective Combinatorial Optimization via Graph-Image Multimodal Fusion},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4sJ2FYE65U},\nnote={under review}\n}" }, "abstract": { "value": "Existing neural multi-objective combinatorial optimization (MOCO) methods still exhibit an optimality gap since they fail to fully exploit the intrinsic features of problem instances. A significant factor contributing to this shortfall is their reliance solely on graph-modal information. To overcome this, we propose a novel graph-image multimodal fusion (GIMF) framework that enhances neural MOCO methods by integrating graph and image information of the problem instances. Our GIMF framework comprises three key components: (1) a constructed coordinate image to better represent the spatial structure of the problem instance, (2) a problem-size adaptive resolution strategy during the image construction process to improve the cross-size generalization of the model, and (3) a multimodal fusion mechanism with modality-specific bottlenecks to efficiently couple graph and image information. We demonstrate the versatility of our GIMF by implementing it with two state-of-the-art neural MOCO backbones. Experimental results on classic MOCO problems show that our GIMF significantly outperforms state-of-the-art neural MOCO methods and exhibits superior generalization capability." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Neural Multi-Objective Combinatorial Optimization", "Multimodal Fusion", "Deep Reinforcement Learning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/a2bbd6277481ae289d269a16e8ccb6fc25bdb1a0.pdf" }, "presentation": null, "primary_area": { "value": "other topics in machine learning (i.e., none of the above)" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Neural Multi-Objective Combinatorial Optimization via Graph-Image Multimodal Fusion" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": { "value": "I have read and agree with the venue's withdrawal policy on behalf of myself and my co-authors." } }, { "TLDR": null, "_bibtex": { "value": "@misc{\nsingh2024unified,\ntitle={Unified Framework for Causal Discovery and Long-term Forecasting in Non-stationary Environments},\nauthor={Har Simrat Singh and Biwei Huang},\nyear={2024},\nurl={https://openreview.net/forum?id=4sJIgdErt1}\n}" }, "abstract": { "value": "Non-stationary data is prevalent in various real-world domains such as climate science, economics, and neuroscience, presenting significant challenges for tasks like forecasting and causal discovery from observational data. Existing approaches often operate under the assumption that the data is stationary. In this work, we introduce a unified framework that combines long-term forecasting and causal discovery with non-linear relations in a non-stationary setting. Specifically, we assume that the nonlinear causal relations in the observed space can be transformed into linear relations in the latent space via projections. In addition, we model the non-stationarity in the system as arising from time-varying causal relations. The proposed model demonstrates that adopting a causal perspective for long-term forecasting not only addresses the limitations of each task but also makes the causal process identifiable, enhances interpretability, and provides more reliable predictions. Moreover, our approach reformulates causal discovery into a scalable, non-parametric deep learning problem. Through experiments on both synthetic and real-world datasets, we show that our framework outperforms baseline methods in both forecasting and causal discovery, underscoring the benefits of this integrated approach." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": { "value": [ "~Har_Simrat_Singh1", "~Biwei_Huang1" ] }, "authors": { "value": [ "Har Simrat Singh", "Biwei Huang" ] }, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "causal discovery", "long-term forecasting" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": { "value": "singh|unified_framework_for_causal_discovery_and_longterm_forecasting_in_nonstationary_environments" }, "pdf": { "value": "/pdf/ac2aaade2cdf5d96e8869e7411d4caba8a274376.pdf" }, "presentation": null, "primary_area": { "value": "causal reasoning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Unified Framework for Causal Discovery and Long-term Forecasting in Non-stationary Environments" }, "venue": { "value": "ICLR 2025 Conference Withdrawn Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Withdrawn_Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "see above." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. One of the paper's main strengths is the formal introduction of the Online Continual Graph Learning (OCGL) framework.\n2. The authors develop a benchmarking environment specifically for OCGL, including multiple datasets and evaluations of various continual learning methods. \n3. The experimental setup and the detailed analysis provided in the paper are thorough and well-constructed." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel framework, OCGL, which addresses the challenges of applying continual learning principles to graph-structured data in an online setting. It innovatively formulates the problem of learning from a continuously evolving graph, emphasizing the necessity to manage computational complexity and avoid catastrophic forgetting—a common issue where a model loses previously learned information upon learning new data. To facilitate research in this area, the authors develop a benchmarking environment with tailored datasets to evaluate various continual learning methods adapted to graph settings. They also propose practical solutions such as neighborhood sampling to maintain computational efficiency as the graph grows." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The proposed problem is novel, however, the detailed appliable scenario for such OCGL framework should be further explained, especially on graph data. \n2. The baselines chosen in this paper are all Continual learning methods. More methods for the online learning setting should be included.\n3. Also, as a benchmark paper, it would be beneficial to introduce more new datasets.\n4. The contribution of this paper seems limited to me. The authors introduced a new problem setting OCGL for graph learning and presented a benchmarking environment for OCGL, but did not propose a novel method to solve this problem. Although I understand benchmarking papers are also important to the research community, I believe that the contribution in this case may not be significantly sufficient for inclusion in this conference.\n5. The third contribution, using random sampling to address the complexity of multi-hop aggregation, is a very easy-to-get idea, and seems trivial to me." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. In the Mini-batching part of Section 3.1, what does it mean by 'L>1 is not in contrast with the growing mechanism of the graph'? I could understand what the authors want to express should be that the entire graph may be required for aggregating multi-hop information, but the writing here seems confusing.\n\n2. It is a little bit confusing whether the proposed strategy allows the model to access the entire graph that contains previous nodes. It is stated that the up-to-date graph Gt is stored in a Past Information Store (PIS) system, but only allow limited use of information from PIS. It is unclear what kind of usage is deemed as 'limited'. Additionally, it is also stated that the PIS is different from a 'eventual memory buffer'. This is also confusing. If the PIS contains the completely graph with all previous information, then what is the role of the 'eventual memory buffer', and why we still need such a buffer?\n\n3. In the 'training details' part in the experiment section, when talking about the batch size, how is each batch used? Given a new task with N data, will the model be trained on the N data for several epochs, and in each epoch, the batches are fed into the model sequentially?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Online continual graph learning has not been fully explored, and this work makes some contribution in this direction. \n\n2. Compared to existing continual graph learning works, this work adopts a more practical hyperparameter selection strategy that only use a few tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to formulate the setting of online continual graph learning considering the efficiency of batch processing and graph topology, and proposes a set of benchmark datasets for online continual graph learning. Additionally, from the technical perspective, the authors address the challenge of GNN memory usage. \n\nWithin the context of online continual graph learning, the graphs are defined as a time series, in which the graph snapshot at each time stamp t contains the nodes and edges collected from the starting time till t. Each new snapshot is created when a new node is added. The newly attached information includes the new node, its neighbors, and the node features." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The main weakness is the inconsistence between the proposed setting and the actual experiments. Although the paper described an online learning setting, but the task construction in experiments is still same as the continual graph learning setting with task boundaries. As mentioned in the paper, 'the graph grow with nodes from two new classes at a time', then the incremental manner is same as a normal class incremental learning instead of an online learning setting. I would recommend that the experiments should be consistent with the proposed setting,in which each new snapshot could contain one node or a mini-batch of new nodes, but not necessarily a new task containing new classes.\n\n2. The adopted baselines are a little bit out of date. Besides, only TWP is specially designed for graph data, while the others don't consider the graph structures. Admittedly the authors have discussed why some baselines are not adopted, but the mentioned ones are all proposed no later than 2021. Therefore, it is not convincing enough that the adopted methods can represent the state-of-the-art performance. I would recommend that the recent continual graph learning works proposed from 2022 to 2024 could be thoroughly investigated, discussed, and compared whenever appropriate." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The introduction of the Online Continual Graph Learning (OCGL) framework extends continual learning to dynamic, graph-structured data.\n\n2. The paper provides a thorough evaluation of multiple continual learning methods, adapting them for online graph learning.\n\n3. The proposed neighborhood sampling strategy effectively addresses the computational and memory challenges of multi-hop neighborhood aggregation in GNNs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces the Online Continual Graph Learning (OCGL) framework to handle non-stationary streaming data in graph structures. It benchmarks several continual learning methods on four datasets, adapting them for the online graph learning scenario. The authors propose a neighborhood sampling strategy to address the issue of neighborhood expansion in Graph Neural Networks (GNNs) and reduce computational complexity." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The benchmarks focus mainly on node classification tasks, and extending the framework to more diverse graph-based applications (e.g., edge prediction, link prediction) could strengthen the paper's contributions.\n\n2. The paper primarily compares traditional continual learning methods adapted for the Online Continual Graph Learning (OCGL) framework. It does not include comparisons with more recent state-of-the-art continual graph learning methods proposed in the recent three years, such as MSCGL[1] and UGCL[2].\n\n [1] J. Cai, X. Wang, C. Guan, Y. Tang, J. Xu, B. Zhong, and W. Zhu, ''Multimodal continual graph learning with neural architecture search,'' in Proceedings of the ACM Web Conference, 2022, pp.1292–1300.\n\n [2] T. D. Hoang, D. V. Tung, D.-H. Nguyen, B.-S. Nguyen, H. H.Nguyen, and H. Le, ''Universal graph continual learning,'' Transactions on Machine Learning Research, 2023.\n\n3. While the sampling strategy improves computational efficiency, it can negatively impact model accuracy. \n\n4. The paper predominantly concentrates on experimental evaluation and lacks an in-depth theoretical analysis of the proposed method's properties, such as convergence, computational complexity, and theoretical bounds on forgetting." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "see weakness" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- the studied problem is an important research question\n\n- I like the attempts that the author tried to take a more systematic approach toward the problem" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces an Online Continual Graph Learning (OCGL) framework designed for learning in dynamic graph environments where data arrives in a streaming fashion. The authors address challenges of catastrophic forgetting in graph-based continual learning, particularly when working with graph neural networks (GNNs) that rely on neighborhood information, which can lead to high memory and computational costs. The proposed OCGL framework is evaluated on four node classification datasets, using modified continual learning methods suited for online learning. They propose neighborhood sampling as a strategy to address neighborhood expansion challenges, which could otherwise lead to prohibitive costs in dynamic graph settings." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. the technical content of the paper does not address the research question proposed by the authors. For example\n a. one of the claimed distributions is the formulation of the so-called online GCL. However, I do not see any formal formulation of the problem. Only a generic description is provided. For example, to fulfil the claim, one would naturally expect to see the data model, the definition and format of the leaner, and the properties and requirements for an effective learner in this scenario. None of these information is provided.\n b. another claim is that online GCL is a new learning paradigm and different from GCL. One would expect to see a detailed comparison between these two. How are they different exactly? How much is the difference?\n\n2. there are some statements that are not factually correct. For example, continual learning is inherently an online setting. An ideal continual learning algorithm should adaptively learn from the new data without the need to access previous data. However, this can be proved theoretically impossible. Therefore, many continual learning algorithms compromise by allowing partial access to historical data. Even for online systems, storing historical data is also allowed. Regarding the task boundary, there have been many studies that looked at the continual learning setting without a clear task boundary. These studies have been under the terms such as \"task-free continual learning\" and \"domain-free continual learning\"[1]. Furthermore, it is not clear what exactly task-boundary means in the paper.\n\n3. the proposed technique is standard and the paper has no conceivable novelty or contribution. The neighbourhood explosion problem is a standard issue in GNN training even for the case of training GNN on static graphs and neighborhood sampling has been de-facto in training GNN. The issue of changing graph structure in GCL has been documented and studied in [2].\n\n[1] \"Cglb: Benchmark tasks for continual graph learning.\" Advances in Neural Information Processing Systems 35 (2022): 13006-13021.\n\n[2] \"Towards robust graph incremental learning on evolving graphs.\" International Conference on Machine Learning. PMLR, 2023." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024online,\ntitle={Online Continual Graph Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4sJJixGIZX},\nnote={under review}\n}" }, "abstract": { "value": "The aim of Continual Learning (CL) is to learn new tasks incrementally while avoiding catastrophic forgetting. Online Continual Learning (OCL) specifically focuses on learning efficiently from a continuous stream of data with shifting distribution. While recent studies explore Continual Learning on graphs exploiting Graph Neural Networks (GNNs), only few of them focus on a streaming setting. Many real-world graphs evolve over time and timely (online) predictions could be required. However, current approaches are not well aligned with the standard OCL literature, partly due to the lack of a clear definition of online continual learning on graphs. In this work, we propose a general formulation for online continual learning on graphs, emphasizing the efficiency of batch processing while accounting for graph topology, providing a grounded setting to analyze different methods. We present a set of benchmark datasets for online continual graph learning, together with the results of several methods in CL literature, adapted to our setting. Additionally, we address the challenge of GNN memory usage, as considering multiple hops of neighborhood aggregation can require access to the entire growing graph, resulting in prohibitive costs for the setting. We thus propose solutions to maintain bounded complexity for efficient online learning." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "continual learning", "online learning", "graph neural network" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/d9ddedd8b36c954be891e37b0a5ef0ffc09f29fe.pdf" }, "presentation": null, "primary_area": { "value": "transfer learning, meta learning, and lifelong learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/7161559470fcdcdf01efae8038e7fff391441842.zip" }, "title": { "value": "Online Continual Graph Learning" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please check the weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This work is pioneering in applying GANs within a non-autoregressive structure for text generation, presenting novel solutions like Position-Aware Self-Modulation and Dependency FFN to tackle inherent limitations in GAN-based text generation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel model called Adversarial Non-autoregressive Transformer (ANT) aimed at enhancing the efficiency and performance of Generative Adversarial Networks (GANs) in text generation. Unlike conventional GANs that rely on autoregressive (AR) structures, ANT leverages a non-autoregressive (NAR) framework, allowing for parallel computation and significantly reducing latency in both training and inference. Key contributions include the introduction of Position-Aware Self-Modulation to enhance representation diversity and Dependency Feed Forward Network (Dependency FFN) to improve dependency modeling. Experimental results show ANT's competitive performance with AR models in terms of quality while achieving lower latency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The datasets chosen for experimental validation are relatively simple, lacking common tasks like translation and summarization, which weakens the persuasiveness of the results.\n2. The issue described in line 57, \"the dynamic weight assignment process becomes unstable during the fragile training of GANs,\" lacks references, in-depth analysis, or detailed description of the phenomenon, making it difficult to thoroughly understand this problem." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "refer to the comments" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The authors propose Position-Aware Self-Modulation (PASM), which provides more diverse and effective latent variable representations, enhancing the model's ability to capture the diversity of different words in sentences.\n- To improve the dependency among the decoding procedure, the authors propose Dependency Feed Forward Network (DFFN), which can lead to better performance.\n- The authors conduct extensive experiments to validate the effectiveness of their proposed model, comparing it against mainstream models and demonstrating its competitive performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the application of Generative Adversarial Networks (GANs) in non-autoregressive (NAR) text generation, addressing the limitations of existing GAN-based text generation models that typically rely on autoregressive structures. The authors identify two main issues with current NAR models: the lack of diversity in latent variable representations and the instability of attention mechanisms during GAN training. To tackle these problems, they introduce two useful techniques: Position-Aware Self-Modulation (PASM) and Dependency Feed Forward Network (DFFN). The experimental results demonstrate that ANT achieves comparable performance to the baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The title of the paper is \"Unlocking the Power of GANs xxx.\" Generally, the strength of GANs lies in the training of the generator and discriminator through a game-theoretic mechanism. However, the main focus of this paper is not on GANs but rather on non-autoregressive text generation. I do not believe the power of GANs lies in non-autoregressive modeling.\n- While the authors compare their model, ANT, to several state-of-the-art models, it appears they have selectively chosen only strong non-autoregressive baselines that utilize GANs. Other baseline methods, such as Huang et al. (ICML 2022), should also be discussed to strengthen the claims regarding the model's superiority.\n- Additionally, the experiments are conducted primarily on specific tasks and datasets, which are somewhat outdated. It would be valuable to assess how well the model generalizes to other text generation tasks, such as summarization, dialogue generation, and long-form text generation." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Could the authors provide empirical evidence or further analysis to support the claim that Position-Aware Self-Modulation significantly improves generation diversity compared to standard practices in NAR models?\n2. How does the Dependency Feed Forward Network provide a clear advantage over traditional FFNs in the context of GAN training, and what experimental results demonstrate this, beyond the marginal improvement shown in Figure 5?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper is well-structured and provides clear explanations of the proposed methods and their implications, making it easy to follow the authors' reasoning." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces the Adversarial Non-autoregressive Transformer (ANT), a GAN-based model for efficient text generation. It proposes two main contributions: Position-Aware Self-Modulation and Dependency Feed Forward Network (Dependency FFN). The study claims that ANT achieves comparable performance to mainstream models with significantly lower latency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper may lack a comprehensive comparison with state-of-the-art non-autoregressive models, which is crucial for establishing the significance of the proposed ANT model: Glancing Transformer (Qian et al, 2021), Fully-NAT (Gu et al. 2021), DA-Transformer (Huang et al. 2022), SUNDAE (Nikolay Savinov et al. 2022), etc. \n2. While the paper claims that Position-Aware Self-Modulation enhances generation diversity, it appears to be a common practice to input identical [MASK] tokens plus positional embeddings in NAR models, which also achieve strong performance during decoding. The paper does not provide direct evidence to show that the similar representation approach hinders the model's generation ability or that Position-Aware Self-Modulation offers a significant improvement over this standard practice. This lack of evidence makes it difficult to assess the true impact of this contribution.\n3. The Dependency Feed Forward Network is presented as a solution to the instability of word dependencies during GAN training. However, the provided evidence in Figure 5 shows only a marginal improvement, with the gap in FED not exceeding 0.001. Such a small difference raises questions about the practical significance of this improvement, especially considering the computational overhead it might introduce." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See weaknesses section" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "This work pioneers the development of language GANs based on non-autoregressive (NAR) structures, addressing the high latency issues inherent in autoregressive (AR) models. By generating all words in parallel, the Adversarial Non-autoregressive Transformer (ANT) achieves high-efficiency generation, significantly reducing both training and inference times.\n\nThe introduction of Position-Aware Self-Modulation and Dependency Feed-forward Network (Dependency FFN) addresses critical challenges in GAN-based NAR models. Position-Aware Self-Modulation enhances the diversity of hidden representations, leading to the generation of more varied and high-quality words in sentences. Dependency FFN improves the stability and accuracy of dependency modeling, resulting in more grammatically coherent outputs compared to traditional attention mechanisms." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work introduced the Adversarial Non-autoregressive Transformer (ANT), a pioneering study of building language GANs based on non-autoregressive (NAR) structures to address the exposure bias problem and reduce latency in training and inference. ANT tackles two key issues: the lack of diversity in latent variable representations by proposing Position-Aware Self-Modulation, and the inaccurate word dependency modeling in Transformers by adopting a Dependency Feed Forward Network. Experimental results show that ANT achieves performance comparable to mainstream models in a single forward pass, with promising applications in latent interpolation and semi-supervised learning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The baselines selected for comparison in the paper are quite outdated, with the chosen non-autoregressive (NAR) models being from 2018, 2019, and 2021. Given the rapid advancements in the field of natural language processing (NLP), it is crucial to compare the proposed model against the most recent and state-of-the-art NAR models to provide a more accurate assessment of its performance. The absence of comparisons with the latest models raises concerns about the relative effectiveness and competitiveness of the proposed approach.\n\nThe experiments conducted in the paper are limited to the COCO and EMNLP datasets, which do not provide a comprehensive evaluation of the model's capabilities. To thoroughly assess the performance and robustness of the proposed NAR model, it is essential to test it on a wider range of datasets, including those for machine translation (e.g., WMT), natural language inference (e.g., SNLI), and text summarization. Evaluating the model on these additional datasets would offer valuable insights into its effectiveness across different NLP tasks, particularly in handling longer texts, which is a critical aspect of many real-world applications. The current dataset selection limits the generalizability and applicability of the findings.\n\nIf these aspects were addressed with more comprehensive experiments, it would significantly improve the evaluation and increase the overall score of the paper." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024unlocking,\ntitle={Unlocking the Power of {GAN}s in Non-Autoregressive Text Generation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4tiTQ33sDH},\nnote={under review}\n}" }, "abstract": { "value": "Generative Adversarial Networks (GANs) have been studied in text generation to tackle the exposure bias problem. Despite their remarkable development, they adopt autoregressive structures so suffering from high latency in both training and inference stages. Although GANs have potential to support efficient generation by adopting non-autoregressive (NAR) structures, their explorations in NAR models are extremely limited. In this work, we conduct pioneering study of building language GANs based on NAR structures. We identify two issues that constrain the performance of GAN-based NAR models. Firstly, existing methods of incorporating latent variables provide highly similar representations which cannot describe the diversity of different words in sentences. We tackle this problem by proposing Position-Aware Self-Modulation, providing more diverse and effective representations. Secondly, the attention mechanism in Transformer cannot accurately build word dependencies in the unstable training of GANs, and we adopt Dependency Feed Forward Network to enhance the model capacity in dependency modeling. Armed with these two facilities, we propose a GAN-based NAR model, Adversarial Non-autoregressive Transformer (ANT). The experimental results demonstrate that ANT can achieve comparable performance with mainstream models in a single forward pass and has great potential in various applications like latent interpolation and semi-supervised learning." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Language GANs", "Non-Autoregressive Model", "Text Generation" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/8b144a25b2e52968b9926e21981469074fa6f33f.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Unlocking the Power of GANs in Non-Autoregressive Text Generation" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "* How to further improve the generalization of local patterns without relying on visual-linguistic models?\n\n* How does the method ensure adaptability to low-resolution videos in different datasets and real-world application scenarios?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* By using image-text alignment and cross-modality attention, this method successfully extracts local patterns that remain consistent across varying visual data, enhancing its ability to detect novel anomalies.\n\n* The State Machine Module (SMM) and motion estimation integrate temporal clues, effectively strengthening the detection capabilities by including sequential information for more accurate anomaly detection.\n\n*By combining visual and textual features in identifying local patterns, the model benefits from enhanced robustness and accuracy across different visual domains." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel framework for video anomaly detection, aiming to improve generalization for detecting new, unseen anomalies by focusing on local patterns rather than global event patterns. Traditional video anomaly detection (VAD) methods often struggle with unseen anomalies, as they primarily analyze global patterns. This framework utilizes image-text alignment and cross-modality attention to identify and refine local patterns while enhancing them with temporal information. Core components include the Image-Text Alignment Module (ITAM), Cross-Modality Attention Module (CMAM), and State Machine Module (SMM). The proposed approach demonstrates superior performance on several benchmark datasets, suggesting it can generalize better to novel anomalies." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The method relies on the detection effect of visual-linguistic modeling (VLM), whereas multi-object image processing may ignore contextual information and affect performance. The authors need to provide more analysis on the ablation of the foundational models.\n\n* The need for multiple layers of modules (e.g., ITAM, CMAM, SMM) to work jointly results in a complex training process that consumes more time and resources. Please provide a comparison of the spatio-temporal complexity analysis with previous methods to demonstrate the practical effectiveness of the method.\n\n* In low-resolution scenes, the generated text description loses detail information, which affects the anomaly detection effect." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "The paper mentions limitations related to the reliance on VLM-based object detectors. How can this limitation be addressed in future work?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The proposed two-stage framework for identifying local patterns is novel and well-motivated. The use of image-text alignment and cross-modality attention is interesting and potentially useful." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a novel framework for video anomaly detection (VAD) that focuses on identifying local patterns to better generalize to unseen anomalies. The framework employs a two-stage process: first, it uses image-text alignment to locate local patterns that are consistent across visual data variances; second, it refines these patterns using cross-modality attention. To further enhance the model, the authors introduce temporal clues through a State Machine Module (SMM) and temporal motion estimation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) The paper lacks a clear discussion of the computational complexity of the proposed framework. Given the use of large language models (LLMs) and other complex modules, it is important to address the efficiency of the approach.\n2) What's the role of State Machine Module (SMM) in temporal sentence generation, there need more detailed explanation of the SMM and its role.\n3) How does the proposed method handle situations with significant occlusions or viewpoint changes, which are common in real-world surveillance videos?\n4) The two-stage process for extracting spatial local patterns using image-text alignment and cross-modality attention is not explained in enough detail. The paper lacks a clear, step-by-step explanation of how these complex processes work." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "None" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. The Global and Local Pattern representations in Figure 1 are hand-drawn, which limits their reliability. Are there any real feature visualization images available instead? Using actual visualizations could better illustrate the motivation and effectiveness of the proposed method, particularly in showing whether it yields more distinguishable local patterns. Figure 1 alone does not provide enough information to convey the method’s motivation and impact.\n2. Utilizing Qwen for cropping bounding box regions based on prompts could significantly impact efficiency.\n3. Is the introduction of the Qwen-Chat model the primary source of performance improvement? My concern is that the proposed method incorporates numerous external models, and it remains unclear whether these additions are the main contributors to the observed performance gains.\n4. Could smaller models be used to replace these large multimodal models? If so, would this result in a significant decrease in performance?\n5. Could you provide statistical results on runtime and efficiency? Does the proposed method have a significant impact on operational efficiency?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The proposed framework is well-structured, with thoughtfully implemented methods. \n2. Experimental results confirm that the approach achieves state-of-the-art performance on established benchmark datasets for video anomaly detection. \n3. The method focuses on fine-grained anomaly features and employs text-image alignment to effectively capture local patterns. \n4. It incorporates Long-range Memory technology, specifically HiPPO, into video anomaly detection." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel framework for video anomaly detection (VAD) that prioritizes identifying local patterns over conventional global patterns. The authors contend that local patterns generalize better to novel anomalies that were not encountered during training. Their proposed approach follows a two-stage process involving image-text alignment and cross-modality attention to efficiently capture and model local patterns. Additionally, the framework includes a State Machine Module (SMM) to integrate temporal dynamics, enabling enhanced anomaly detection by leveraging both spatial and temporal cues. Experimental results show that this approach achieves state-of-the-art performance on well-established benchmark datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The core of the proposed SMM module comes from works like HiPPO, so it seems that the proposed SMM is directly applying these modules to the VAD task. \n2. Both the Image-Text Alignment Module and Cross-Modality Attention Module are based on pre-existing techniques, which limits the methodological innovation.\n3. Is the observed performance improvement attributed to the additional large vision-language models, such as Qwen-VL and BLIP2? The comparison may not be entirely fair. It would be beneficial if the authors could provide evidence or experimental results to clarify whether these powerful external models are the primary contributors to the performance gains.\n4. The motivation for the work lacks clarity. How do the image-text alignment and cross-modality attention modules achieve “identification of local patterns that are consistent across domains and generalize well”? Additionally, how do they contribute to “generalizing model representations to novel anomalies”?\n5. Certain claims may require further validation, such as the statement: “the complementary relation between visual and textual features remains underexplored.”\n6. The paper lacks runtime and efficiency analysis. The code introduction is incomplete, and several experimental details are missing, such as the specific version and scale of Qwen-VL used." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "My understanding of this field is unprofessional. So I will further follow the opinions of other reviewers." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. The two-stage training method of this paper is reasonable. And allow for more fine-grained local features.\n2. This paper gives a lot of visualizations to make it easier to understand the specific content.\n3. The paper has achieved good performance, and the ablation experiment is given." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This article is about video anomaly detection (VAD). This paper proposes a framework for recognizing local patterns, which can be generalized to new samples and dynamic modeling of local patterns. This paper proposes image-text alignment and cross-modal attention. Generalizable representations are built by focusing on textual information features that filter out unnecessary differences in visual data. In addition, time motion estimation complements spatial local models to detect anomalies characterized by new spatial distributions or unique dynamics. A large number of experiments have verified the effectiveness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Not giving motivation for each part of the method. In my opinion, a good paper should give a specific reason and then introduce the method.\n2. The efficiency of the model is worth discussing. You have proposed a lot of model modules. How much more reasoning time will they add to the network?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024local,\ntitle={Local Patterns Generalize Better for Novel Anomalies},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4ua4wyAQLm},\nnote={under review}\n}" }, "abstract": { "value": "Video anomaly detection (VAD) aims at identifying novel actions or events which are unseen during training. Existing mainstream VAD techniques typically focus on the global patterns of events but struggle to generalize to novel samples. In this paper, we propose a framework that identifies the local patterns which generalize to novel samples and models the dynamics of local patterns. The capability of extracting spatial local patterns is achieved through a two-stage process involving image-text alignment and cross-modality attention. Generalizable representations are built by focusing on text-informative features that filter out unnecessary visual data variances. To enhance spatial local patterns with temporal clues, we introduce a State Machine Module (SMM) that combines tokens from different moments to improve sentence generation within cross-modality attention. Furthermore, temporal motion estimation complements spatial local patterns to detect anomalies characterized by novel spatial distributions or distinctive dynamics. Extensive experiments on popular benchmark datasets demonstrate the achievement of state-of-the-art performance. Code is available at https://anonymous.4open.science/r/Local-Patterns-Generalize-Better-1E30/." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Global Patterns; Local Patterns; Image-Text Alignment Module; Cross-Modality Attention; Temporal Sentence Generation; State Machine Module" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/d6c777f6b718a52624a5914f78b2bbfb2c7dda69.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Local Patterns Generalize Better for Novel Anomalies" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "- Here the causal concept effect is considered the ground truth in some sense. Then would it make sense to directly explain the model prediction using the causal concept effect?\n- For the dataset level faithfulness, instead of averaging question level faithfulness, why not directly measure PCC of all examples in the dataset?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Inspecting more finegrained faithfulness is a novel contribution and it allows us to gain better understanding of specific biases in model explanations.\n- The paper proposes a principled method to quantify faithfulness based on counterfactual examples.\n- The finding that safety alignment can make the model hide true reasons (e.g., gender bias) in its explanation (thus is only a form of shallow alignment) is very interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to measure the faithfulness of LLM generated natural language explanations in terms of specific concepts mentioned in it. Specifically, faithfulness is measured by the correlation between the causal effect of a concept (measured by counterfactual predictions) and the likelihood of the concept being mentioned in the explanation. This analysis produces several interesting results, e.g., the model doesn’t mention gender in its explanation despite gender having large causal effect on its prediction; safety alignment can affect model explanation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- It is unclear how the explanations are generated, e.g., are these CoT from zero shot or few shot prompting? Is explanation generated before or after model prediction? It would be interesting to analyze how different prompting methods change the result or improve faithfulness.\n\nMinor:\n- 152: distinct -> disentangled might be a more precise word\n- 194: typo: x in the equation should be in vector form" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. One of the decisions in the paper is to use an auxiliary model to extract concepts, propose a list of alternative values for each concept. Why is this necessary and is there any assumption or requirement that helped settling for a GPT-4o as the auxiliary LLM? The authors could better motivate the selection of GPT-4o in their paper, perhaps by including a small human study comparing the effectiveness of different models in extracting concepts and creating the list of alternate values. The authors should also consider including the prompt used to extract concepts and concept values in the Appendix.\n2. In line 218, the authors mention the use of auxiliary LLM to “list distinct concepts in the context of x”. What kind of verifications were performed to ensure that the extracted concepts and their list of concepts were meaningful? The authors should consider adding a list of extracted concepts and their list of values to the Appendix. They should also consider adding more details about the validation (e.g., manual validation or llm-as-a-judge approach).\n3. Similarly, to the two questions above, in line 224-225, the authors mention “to generate each counterfactual, we instruct [auxiliary LLM] to edit the question x by changing the value of [concept] ..., while keeping everything else the same”. However there seems to be no validation of this claim. Did the authors validate that the perturbed input x was indeed minimally distant from x? If not, the authors should consider including such analysis, perhaps by showing the minimum edit distance or n gram overlap between the modified and original inputs.\n4. Why did the authors select a linear correlation coefficient as opposed to a non-linear coefficient?\n5. In Figure 1 (right), we observe that different behavioral concepts end in different regions of the scatter plot (there are orange points in the top and orange points around EE in [-0.5, -1.5]. Is there any insight or pattern that justify why there are different clusters? Could it be that the model is less prone to use some concepts for specific demographics?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Causally inspired definition and methodology to assess the faithfulness of LLM-generated explanations.\n- Empirical validation of proposed methodology in two different question answering datasets.\n- The finding that GPT-3.5 produces more faithful explanations (at a dataset-level) than the more recent and advanced models (GPT-4o and Claude 3.5 sonnet) is interesting. They also show that unfaithful explanations by GPT-3.5 is more harmful than GPT-4o\n- The analysis concerning the impact of different types of interventions (i.e., remove concept vs swap it with a different value) is interesting, revealing the brittleness of safety guards employed in GPT-3.5 and GPT-4o." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "**Summary**:\nThis paper adopts a causal inference approach to define and evaluate the faithfulness of LLM-generated explanation in the context of two question answering tasks. The obtained results X.\n\n**Main contributions**: The main contributions are the definition and methodology proposed to assess the faithfulness of explanations.\n\n**Methodology**:\n- Key to the methodology is the assumption that a model is faithful if its explanations consist of only concepts that are impactful for the decision (i.e., have large causal effects) (lines 183-185).\n- The authors first compute the causal effect associated with each concept in the explainability (CE). An auxiliary LLM is used to determine the _explainable_ concepts and produce counterfactual perturbations for each input x. CE is then estimated by contrasting the distributional differences between LLM responses when given the modified inputs vs the original inputs.\n- Then the authors determine the prevalence of each concept appearing in the explanation (EE).\n- Finally, the authors determine the linear alignment between CE and EE (dubbed causal concept faithfulness) for each example using the pearson correlation coefficient. Dataset-level faithfulness score is the average over the examples.\n\n**Writing**: Overall the writing is clear and easy to follow! The authors did a good job in exposing the ideas. Consider the following comments to further increase clarity:\n- Add information about when the experiments were run with each model.\n- lines 321-323: you describe the colors for each of the concept categories. However there seems to be a mismatch between the category color in the image and the color described in text." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **Insufficient validation of the proposed approach**: the authors mention that their method can be used to quantify and discover interpretable patterns of unfaithfulness. However, there is no guarantee that the methodology detects truly unfaithful concepts. To further ensure the correctness of the approach, it would be nice to show linear agreement between CE and EE in a controlled setting where LLMs are known to only produce faithful explanations (e.g., unbiased setting).\n2. **Important parameters of the experiments are not clear in the paper, which may affect reproducibility of the experiments**. The authors could consider providing additional details about the exact number of perturbations generated for each example, the number of generations used to estimate P(Y|X) (during the definition of CE), the decoding hyperparameters (including the temperature and max number of tokens). Additional details should also be provided about how each response y is parsed – this is particularly relevant given that the evaluated models are known to produce nuanced and semantically equivalent texts.\n2. **Small evaluation set and concerns about generalizability**: for the two question-answering, the authors estimate the causal effects (CE) and explanation-implied (EE) effects for 30 examples (using 50 generations per each dataset). However, it is unclear how robust these results are and whether these would generalize to larger models. Perhaps the authors could show how the faithfulness score varies as the number of datapoints increases, therefore, providing an idea of the variability and stability of the scores.\n3. **Univariate counterfactuals**: if I understood correctly, the proposed framework focuses on perturbing the sentences one concept at a time, irrespective of the correlations between features. However, this fails to account for the correlations between different features (e.g., name of schools or organizations is related to socio-demographic features). \n4. **Generalizability to open-source models**: the paper carries analyses on two OpenAI models (GPT-3.5, GPT-4o) and one Anthropic model (Claude-3.5-sonnet). Could be interesting to contextualize the faithfulness of existing open source models (e.g., Llama 3.1) and draw comparisons with closed-source ones." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- In Figure 1, it appears as though the correlation between EE and CE would be significantly lower if done independently for each concept, and then averaged. My question is: is calculating faithfulness on a per-concept basis is possible with your method?\n- And a related question, given that pearson correlation only measures to what extent points lie on *a* line, and not on *the* line y=x, is it the most appropriate metric for you use case, did you consider others?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Precise definition of what is meant by faithfulness.\n- 'causal concept faithfulness' as proposed will be useful for the explainability community.\n- The paper is written well, while being information-dense." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "A formulation of faithfulness is presented called causal concept faithfulness. According to this formulation, when a model produces a natural language explanation of its behaviour that appeals to certain concepts (e.g. gender), altering the input to have a different concept value (e.g. changing the gender of person mentioned in the text), should also alter the behaviour. Thus, under this formulation, a model is faithful if and only if it appeals to exactly those concepts that—if altered—would actually change its behaviour. To measure faithfulness the correlation between two metrics is used: (1) the probability that a concept is mentioned in an explanation; and (2) the actual change after altering the inputs, the KL divergence between the model's output distribution before and after alteration is used. \nTo avoid having to measure these values on very large datasets, the authors propose to use a Bayesian hierarchical model, which 'partially pools' information across interventions for related concepts.\nExperiments are performed on two tasks. The first is a task designed to elicit unfaithful explanations. Models perform poorly w.r.t. two out of three concepts. In the second task, models are shown to have limited faithfulness when explaining their answers for medical question answering." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The use of GPT4o to generate counterfactual inputs is not evaluated independently." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1. Can a faithful explanation be biased? For example, suppose if both CE and EE are high for the example in Table 1, and if the model would have answered Male: 26% Female: 74%, with Explanation References: Traits/Skills: 15% Age: 0% Gender: 85%. This is a clear case of gender bias, but can we say the explanation is faithful here referring to Definition 2.3?\n2. How can we understand the observations if the models memorized these benchmark datasets during their training stage? Does this imply that the model’s reasoning process is so vulnerable to bias that it can even disregard a previously memorized correct answer?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "I really enjoyed reading the paper! It addresses a highly relevant topic of faithfulness in the current landscape of AI. The writing is engaging and clear. \n\nOne of its standout features is its approach to a critical issue: it offers a concrete and measurable method for assessing explanation faithfulness in large language models, an area that has been difficult to define in previous research. By introducing the concept of causal concept faithfulness, the authors provide a way to evaluate how \"honest\" a model's explanations are, while also revealing specific patterns of misleading explanations." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper investigates ‘unfaithfulness’ of SOTA LLMs. In this paper, the authors introduce a new metric for measuring the faithfulness of a model, namely, causal concept faithfulness that not only quantifies but also reveals semantic patterns of unfaithfulness. To uncover these patterns, they put this method to the test on two tasks - a social bias task and a medical QA task to demonstrate how decisions made by the models change along with the provided explanations to justify the wrong decisions made by the model." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**High level comments**\n1. It remains uncertain whether biases impact all types of reasoning tasks uniformly or if certain domains are more affected than others.\n2. Moreover, the experiments do not specify how these findings may apply beyond classification tasks to biases that could affect other generative tasks.\n\n***Minor comment***\nLine 321 typo -> should be orange for behavior, red for identity" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce a novel method for measuring the faithfulness of explanations given by LLMs." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024walk,\ntitle={Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4ub9gpx9xw},\nnote={under review}\n}" }, "abstract": { "value": "Large language models (LLMs) are capable of generating *plausible* explanations of how they arrived at an answer to a question. However, these explanations can misrepresent the model's \"reasoning\" process, i.e., they can be *unfaithful*. This, in turn, can lead to over-trust and misuse. We introduce a new approach for measuring the faithfulness of LLM explanations. First, we provide a rigorous definition of faithfulness. Since LLM explanations mimic human explanations, they often reference high-level *concepts* in the input question that purportedly influenced the model. We define faithfulness in terms of the difference between the set of concepts that the LLM's *explanations imply* are influential and the set that *truly* are. Second, we present a novel method for estimating faithfulness that is based on: (1) using an auxiliary LLM to modify the values of concepts within model inputs to create realistic counterfactuals, and (2) using a hierarchical Bayesian model to quantify the causal effects of concepts at both the example- and dataset-level. Our experiments show that our method can be used to quantify and discover interpretable patterns of unfaithfulness. On a social bias task, we uncover cases where LLM explanations hide the influence of social bias. On a medical question answering task, we uncover cases where LLMs provide false claims about which pieces of evidence influenced its decisions." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "large language models", "faithful explanations", "explainability", "safety", "counterfactual reasoning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/54c32c06783e40072d28371abc4308858c27cd4f.pdf" }, "presentation": null, "primary_area": { "value": "interpretability and explainable AI" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "N.A." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper is well-motivated.\n2. The proposed method is simple and effective.\n3. The inclusion of theoretical analysis strengthens the work.\n4. Extensive experiments show the effectiveness, scalability and robustness.\n5. This paper is easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces DUALFormer, a novel Graph Transformer model designed to address scalability challenges and improve local-global information fusion. The approach is both simple and theoretically grounded. Extensive experiments demonstrate DUALFormer’s effectiveness, scalability, and robustness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The proposed method can be interpreted as \"attention on attributes\". I wonder how is it different from the standard self attention. Especially why it can perform better on node classification? And when it is expected to perform better and when not?\n2. Can you provide further analysis, such as case studies, to further explain the semantic meanings of the \"attention on attributes\"?\n3. Can you provide further analysis and empirical studies to show that the GNNs after the graph Transform can indeed learn the localities in graphs?\n\nI will raise my score if my concerns are properly addressed." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- The first question concerns the reasonableness of applying softmax to the global correlations between features.\n\n - In standard self-attention, $ \\mathbf{O} = \\exp(\\text{sim}(\\mathbf{Q}, \\mathbf{K}))\\mathbf{V} $ (Eq. 6).\n - Through linearized attention, $ \\mathbf{O} = \\phi(\\mathbf{Q}) \\phi(\\mathbf{K})^\\top \\mathbf{V} $ (Eq. 11), where each element in $ \\phi(\\mathbf{Q}) \\phi(\\mathbf{K})^\\top $ is non-negative, representing attention weights (global dependencies between nodes).\n - By the commutative property of matrix multiplication, $ \\mathbf{O} = \\phi(\\mathbf{Q}) (\\phi(\\mathbf{K})^\\top \\mathbf{V}) $, so we can interpret $ (\\phi(\\mathbf{K})^\\top \\mathbf{V}) $ as a correlation matrix (with elements that can be positive or negative).\n\n However, in Eq. 13, $ \\mathbf{V} \\text{softmax}(\\mathbf{Q}^\\top \\mathbf{K}) $, i.e., $ \\mathbf{Q} \\text{softmax}(\\mathbf{K}^\\top \\mathbf{V}) $, differs from $ \\phi(\\mathbf{Q}) (\\phi(\\mathbf{K})^\\top \\mathbf{V}) $ because elements in $\\text{softmax}(\\mathbf{K}^\\top \\mathbf{V}) $ are all non-negative, unlike those in $ (\\phi(\\mathbf{K})^\\top \\mathbf{V})$. Could you clarify these differences and explain why it is reasonable to replace $ \\phi(\\mathbf{Q}) (\\phi(\\mathbf{K})^\\top \\mathbf{V}) $ with $ \\mathbf{Q} \\text{softmax}(\\mathbf{K}^\\top \\mathbf{V}) $?\n\n- The second question pertains to the interpretation of the proposed global attention. The method appears to aggregate information along the feature dimension, unlike previous approaches that gather global information across all or most nodes in a graph. For a one-dimensional feature, $ \\mathbf{V} \\text{softmax}(\\mathbf{Q} \\mathbf{K}^T) $ in Eq. 13 reduces to $ \\mathbf{V} \\cdot \\alpha $, where $ \\alpha $ is a scalar and $ \\mathbf{V} \\in \\mathbb{R}^{n} $. How can this be understood as gathering information from a global perspective?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The writing is generally clear and accessible, making the paper readable and easy to follow.\n- The proposed method is both understandable and implementable, yet effective. It performs well on several datasets.\n- The paper includes diverse experimental analyses, such as node classification, node property prediction, ablation studies, and parameter sensitivity analyses. Furthermore, the authors offer theoretical guarantees to support the method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "To address the scalability limitations of graph transformers (GTs) and the challenge of balancing local and global information, this paper introduces DualFormer, a novel GT architecture. DualFormer calculates global attention along the feature dimension, enabling the model to perform effectively and efficiently on large graphs while maintaining strong performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The motivation for the study is not fully convincing. Further details are provided in the questions below.\n- Since the paper emphasizes the method’s scalability, additional experiments on larger graphs would reinforce this claim. Suggested datasets include *Roman-Empire*, *Question[1]*, *Wiki*, and *ogbn-papers100M*. Moreover, the GNN baselines in Tables 2 and 3 are outdated, which may reduce the persuasiveness of the results. For instance, the statement, “Most GTs consistently show superior performance over GNNs across all datasets” (line 451), would be more convincing if compared with recent GNN baselines, such as *ChebNetII[2]* and *OptBasis[3]*, to present a more comprehensive evaluation.\n- Minor Issues: There are a few typographical errors, such as \"abov\" (line 182). Consistent notation throughout the paper is also preferable. For instance, in line 168, there is a \"$\\times$\" symbol between a scalar and a matrix, but not in line 216. Additionally, line 191 includes a \"$\\cdot$\" between matrices, whereas line 167 does not.\n\n[1] A critical look at the evaluation of GNNs under heterophily: Are we really making progress? In ICLR 2023.\n\n[2] Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited. In NeurIPS 2022.\n\n[3] Graph Neural Networks with Learnable and Optimal Polynomial Bases. In ICML 2023." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "See the above 'Weaknesses'" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1) The motivation for the dual design of local and global modules in this paper is clear and interesting.\n2) The model DUALFormer is simple and efficient with a solid theoretical foundation. \n3) The paper offers extensive experimental validation across various datasets. \n4) The paper is well-organized and easy to read." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces DUALFormer, a graph transformer that tackles the challenges of the scalability and trade-off between local and global expressivity faced by current models. The motivation is to model the global dependencies among nodes by approximately characterizing the correlations between features. DUALFormer adopts a simple, intuitive design that includes local graph convolutional networks operating on the node dimension and a global self-attention mechanism operating on the feature dimension. The effectiveness and efficiency of the proposed DUALFormer are demonstrated in experimental evaluations across node classification and node property prediction tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) The paper has some minor errors that need fixing. For example, Table 2 misses the mean value for the GraphGPS model on the Citeseer dataset. \n2) To enhance readability, Equation 13 should be split into two or three equations. \n3) The model DUALFormer places the GNN layers, such as the SGC layers, after the attention layers. What is the rationale behind this design? Is it possible to reverse this order? \n4) Figure 4 shows that the model utilizing APPNP outperforms the one using SGC in the Cora and Pubmed datasets. What accounts for this performance difference?\n5) The effect of certain hyper-parameters, such as the parameter $\\alpha$ in Equation 13, on performance has yet to be unverified. \n6) The paper does not mention any plans to open-source the code." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I have the following questions:\n1. As the authors claim in Eq. 13, the proposed method only captures the feature-to-feature correlations. In my opinion, it is not the global information on the graph since it is unable to capture the relations between nodes. Why do authors claim the proposed method can capture the global information on the graph?\n2. According to the paper, the efficiency is the most important contribution of the proposed method. I think the authors express this point in a wrong way. Firstly, the authors claim that the computational complexity of the proposed method is $O(n)$ which is obviously wrong. Based on Eq. 14, the calculation involves the adjacency matrix. Hence, the computational complexity of this part is $O(E)$ and it is cannot be ignored since $|E|>|N|$ （even $|E|>>|N|$ on some graphs). Then, the authors only compare the time cost of each epoch to demonstrate the efficiency which is not reasonable. I think the total training time cost is the most important metric to demonstrate the efficiency of a method. So, the authors should report the overall training cost of each method for efficiency study, especially on large-scale graphs. Maybe authors can refer to the settings in NAGphormer. For instance, can the proposed method achieve more efficient and more powerful performance than NAGphormer on Aminer, Reddit and Amazon2M?\n3. As shown in Section 4.2, DUALFormer relies on the sampling strategy to perform on large-scale graphs, just like advanced linear graph Transformers. Hence, I think the GPU memory comparison is questionable since it is largely related to the batchsize. Do authors set the same batch for each method?\n4. The analysis of the $\\alpha$ is missing. According to Table 5, the performance of DUALFormer could be sensitive to the value of $\\alpha$. So, the parameter analysis of $\\alpha$ should be added into the experiment section." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. This paper is easy to follow.\n2. The authors provide the theoretical analysis.\n3. The results on various datasets seem to be promising." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper develop a new architecture based on GNNs and modified Transformers. The authors conduct expensive experiments as well as theoretical analysis to show the effectiveness of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The comparison of efficiency study seems to be not reasonable.\n2. The key contributions of the proposed method are not clear.\n3. The complexity analysis of the proposed method seems to be wrong. " }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024dualformer,\ntitle={{DUALF}ormer: A Dual Graph Convolution and Attention Network for Node Classification},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4v4RcAODj9},\nnote={under review}\n}" }, "abstract": { "value": "Graph Transformers (GTs), adept at capturing the locality and globality of graphs, have shown promising potential in node classification tasks. Most state-of-the-art GTs succeed through integrating local Graph Neural Networks (GNNs) with their global Self-Attention (SA) modules to enhance structural awareness. Nonetheless, this architecture faces limitations arising from scalability challenges and the trade-off between capturing local and global information. On the one hand, the quadratic complexity associated with the SA modules poses a significant challenge for many GTs, particularly when scaling them to large-scale graphs. Numerous GTs necessitated a compromise, relinquishing certain aspects of their expressivity to garner computational efficiency. On the other hand, GTs face challenges in maintaining detailed local structural information while capturing long-range dependencies. As a result, they typically require significant computational costs to balance the local and global expressivity. To address these limitations, this paper introduces a novel GT architecture, dubbed DUALFormer, featuring a dual-dimensional design of its GNN and SA modules. Leveraging approximation theory from Linearized Transformers and treating the query as the surrogate representation of node features, DUALFormer \\emph{efficiently} performs the computationally intensive global SA module on feature dimensions. Furthermore, by such a separation of local and global modules into dual dimensions, DUALFormer achieves a natural balance between local and global expressivity. In theory, DUALFormer can reduce intra-class variance, thereby enhancing the discriminability of node representations. Extensive experiments on eleven real-world datasets demonstrate its effectiveness and efficiency over existing state-of-the-art GTs." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Graph Transformers", "Node Classification" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/2c2ea3c30784e242967919a718d627d9952bc717.pdf" }, "presentation": null, "primary_area": { "value": "learning on graphs and other geometries & topologies" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "DUALFormer: A Dual Graph Convolution and Attention Network for Node Classification" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see Weaknesses." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper introduces a fresh perspective on equivariant layer characterization by applying irreducible representations and Schur’s lemma to obtain simplified derivations of established models, such as DeepSets, 2-IGN, and Deep Weight Space (DWS) networks.\n\n2. The theoretical foundations are well-developed. The work provides a complete characterization of equivariant layers in the context of unaligned symmetric sets, which is an interesting theoretical contribution." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces an alternative approach for characterizing equivariant linear layers in neural networks that process permutation and related group representations. The paper derives a simpler method for obtaining existing models such as DeepSets, 2-IGN, and Deep Weight Space networks, based on irreducible representations and Schur’s lemma. The proposed framework also considers unaligned symmetric sets, that build upon equivariance to the wreath product of groups." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The presentation and flow of the paper could be improved. The claims and results are challenging to follow, which may limit the broader audience’s ability to appreciate the work.\n\n2. The paper’s contributions lack clarity. The paper offers an irreducible-based derivation for existing results and characterizes equivariant functions on unaligned symmetric elements, but the impact and relevance of these contributions remain unclear. It is not evident how these results benefit the design of novel architectures or enhance our understanding of current ones. This limits the significance of the work and may fall short of ICLR’s standards.\n\n3. The empirical evaluation is limited, and the results are not compelling. Using synthetic data for anomaly detection does not sufficiently demonstrate the method’s practical applicability, as the task is relatively unchallenging and does not show the strengths of the proposed approach." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "* Can the method be generalized to higher-order $k$-IGN in a principled manner? Can you briefly describe the claim that ``using irreducibles could lead to new equivariant models with intermediate irreducible features of lower dimensions''?\n\n* Can you conduct more experiments on real-world and large-scale datasets, and include more baseline? In addition, can you intuitively explain why non-Siamese layers help in these tasks?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper offers the irreducible representations perspective for deriving classical models like DeepSets, 2-IGN and DWS networks. Some derivations are simpler than the original ones. The writing is clear and easy to follow. I check with the details and they are sound." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper studies equivariant linear layers for representations of permutations and related groups from a novel irreducible representations perspective. The authors provide an alternative derivation for models including DeepSets, 2-IGN, and Deep Weight Space (DWS) networks. The theory is then extended to unaligned symmetric sets, showing that there is a vast number of additional non-Siamese layers in certain settings. Experiments show that additional non-Siamese layers improve the performance in tasks like graph anomaly detection, weight space alignment, and learning Wasserstein distances." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* While the new derivations align with original methods, the resulting models are not new. The concept of ``irreducible representation'' is also well studied, so the contribution of the paper lies mainly in bridging two topics, which is interesting but natural. In particular for equivariant graph layers, the authors only provide derivations for 2-IGN. As admitted in the limitation section, the paper does not involve higher-order $k$-IGN. The author should explain whether their method is broadly applicable for these networks based on tensor representations, or need case-by-case derivations.\n\n* Although this is a theoretical paper, the experiments could be improved. More baselines and more real-world tasks are strongly encouraged." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. I find the methodology presented in L135-155 valuable to the research community due to its generalizability beyond the provided examples, most of which are already characterized. For this reason, would it be possible to add a *brief* discussion on the generalization of this methodology to strengthen the impact of this contribution and broaden its relevance to a wider community? See the following for more specific questions.\n2. Computing a basis compatible with the irreducible representation decomposition can be challenging. Does this difficulty limit the methodology’s generalization? Are there similar technical challenges for characterizing $k$-IGN layers for $k > 2$?\n3. Can this methodology be applied to other groups beyond $S_n$ and wreath products? If so, could you briefly provide a few examples?\n4. Representations of the symmetric group are relevant in machine learning and its irreducible representation are absolutely irreducible. In contrast, other relevant groups, such as finite cyclic groups, have real irreducible representations that are not absolutely irreducible. Could the framework presented here be extend to these cases? What potential challenges do you envision in extending to non-absolutely irreducible representations?\n5. Could you elaborate on the future directions for $k$-IGNs presented in the conclusions (L537-539)?\n\n**Minor Issues (No Impact on Recommendation):**\n- L073: I recommend specifying \"$2$-IGNs\" for transparency.\n- L183: Is the presentation of $P_\\tau$ unnecessary?\n- L340: The wreath product of groups is introduced but not defined in detail; as this operation is uncommon in machine learning literature, additional explanation would benefit Section 5. Also, consider demonstrating that equation 7 forms a linear representation of this group, perhaps in the appendix.\n- L420: Typo, “is prove”.\n- L379 and L1030: I cannot understand why $\\mathcal{V}^k$ is an irreducible representation of $\\mathcal{G}^k$; is it instead irreducible for $\\mathcal{G} \\wr S_n$?\n- L1040: The closing curly bracket is missing." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "- Clear presentation and notation, supported by rigorous proofs.\n- The methodology is both valuable and simple, with potential to generalize beyond the examples presented.\n- A novel and complete characterization of representations for unaligned symmetric elements." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces a novel methodology for characterizing equivariant linear layers for permutation representations, utilizing classical results from representation theory. Specifically, it provides an alternative characterization of equivariant linear layers for DeepSets, $2$-IGNs, and DWSNets, as well as the first comprehensive characterization of equivariant linear layers for unaligned symmetric elements. Importantly, the authors identify novel non-Siamese layers and empirically assess their impact." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Lacks discussion on extending the approach to groups and representations beyond the few presented.\n- In particular, an appropriate discussion on characterizing the more expressive layers of $k$-IGNs for $k>2$ is missing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "I would like the authors to comment on the above points regarding novelty and significance of experiments. \n\nMy current opinion is that the work is exceptionally well-written, and bears several contributions to the geometric deep learning literature. However, I am concerned with the novelty and significance, as outlined above. Still, I am leaning towards accepting the paper, but would like to hear from the authors about my points of criticism." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "-The paper is exceptionally well written. The language is clear and concise, the sections are structured, and the mathematical formalism/notation is elegant. \n\n-The problem considered is a fundamental one in machine learning literature. Constructing (linear) equivariant maps lies at the heart of geometric deep learning, which has been successful in several applications. \n\n-The proposed solution is general, as it applies, in principle, to any input/output group representation. Several existing frameworks are phrased under the same paradigm, contributing with structure and clarity to the geometric deep learning literature." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper considers the problem of constructing linear equivariant layers for groups acting (linearly) on input and output spaces. Specifically, it proposes to exploit the decomposition into irreducible group representations and then appealing to Schur’s Lemma, which reduces the problem to choosing coefficients for pairs of isomorphic representations. Several specific instances are analyzed, such as permutation groups in the context of graph neural networks, groups acting on weights of deep networks, and wreath products acting on products of representations." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I believe that the proposed approach via Schur’s Lemma comes with disadvantages. To begin with, using Schur’s Lemma to construct equivariant linear maps is not novel in the geometric deep learning community. It is a rather well-known technique – see, for example, Behboodi et al., Section 3.2. This is a major concern, since Schur’s Lemma represents a core point of this work; the other contributions amount to rephrasings of known frameworks from the literature under the lenses of Schur’s Lemma. Moreover, Schur’s Lemma has some restrictions. First, it requires the decomposition into irreducible representations to be known a priori, which is not always the case. Such decomposition is challenging to compute algorithmically for general groups and representations. Second, Schur’s Lemma applies naively only to complex representations (i.e., over $\\mathbb{C}$). As the authors mention, this is not an issue for permutation groups (appendix B), but it can be for other groups. It is still possible to apply Schur’s Lemma to arbitrary real representations of arbitrary groups, but this involves subtleties – see Behboodi et al., Section 8. \n\nI also find the experimental section rather weak. The experiments reported only consider ideal equivariant tasks, i.e., scenarios where the ground-truth function is equivariant. The experimental results show that adding equivariant layers to the network improves (generalization) performance, as compared to non-equivariant architectures. This is not surprising, since in these cases the inductive bias given by equivariance aligns perfectly with the structure of the task. In typical real-world scenarios (e.g., image classification), the (highly-noisy) ground-truth function is instead not exactly equivariant, or it is not equivariant on all the input data. In my opinion, it would be more informative and less trivial to test the models on these types of real-world tasks. The equivariance bias is often still beneficial in terms of generalization – as works in geometric deep learning have extensively shown – but empirical investigations are required to assess this carefully. \n\nMinor typos: \n\n-The paragraph title on line 86 is not capitalized, while the one on line 100 is. \n\n-The tables in section 6 exceed the margins of the paper.\n\n\nBehboodi et al., A PAC-Bayesian Generalization Bound for Equivariant Networks, NeurIPS 2022." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Characterization of all permutation equivariant linear mappings using irreducibles approach on several use-cases." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024revisiting,\ntitle={{REVISITING} {MULTI}-{PERMUTATION} {EQUIVARIANCE} {THROUGH} {THE} {LENS} {OF} {IRREDUCIBLE} {REPRESENTATIONS}},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4v4nmYWzBa},\nnote={under review}\n}" }, "abstract": { "value": "This paper explores the characterization of equivariant linear layers for representations of permutations and related groups. Unlike traditional approaches,\nwhich address these problems using parameter-sharing, we consider an alternative\nmethodology based on irreducible representations and Schur’s lemma. Using this\nmethodology, we obtain an alternative derivation for existing models like DeepSets,\n2-IGN graph equivariant networks, and Deep Weight Space (DWS) networks. The\nderivation for DWS networks is significantly simpler than that of previous results.\nNext, we extend our approach to unaligned symmetric sets, where equivariance\nto the wreath product of groups is required. Previous works have addressed this\nproblem in a rather restrictive setting, in which almost all wreath equivariant layers\nare Siamese. In contrast, we give a full characterization of layers in this case and\nshow that there is a vast number of additional non-Siamese layers in some settings.\nWe also show empirically that these additional non-Siamese layers can improve\nperformance in tasks like graph anomaly detection, weight space alignment, and\nlearning Wasserstein distances." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "deep weight spaces", "permutation equivariance", "irredicible representations." ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/37b7ce376b8cc287941bdd0cb02b074f952702fb.pdf" }, "presentation": null, "primary_area": { "value": "learning on graphs and other geometries & topologies" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/0d5a43a77051139145697ec92923f0c11bccab19.zip" }, "title": { "value": "REVISITING MULTI-PERMUTATION EQUIVARIANCE THROUGH THE LENS OF IRREDUCIBLE REPRESENTATIONS" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Why was VDN specifically chosen as the base MARL algorithm, given its known limitations in representation capacity? How would the proposed approach perform with more advanced MARL algorithms like MAPPO, IPPO, or QMIX?\n2. Given that the experiments were conducted only on MPE environments (Spread-v3, Tag-v3, Reference-v3), how would the method perform on more complex MARL benchmarks? What challenges do you anticipate, and how sensitive might performance be to the choice of hyperparameters $\\alpha$ and $\\beta$?\n3. What policy was used to generate responses for collecting preference feedback?\n4. How was the preference feedback collected? Was it synthetic, based on true environment rewards, or did it come from real human preferences? These details are crucial for reproducibility, a deeper understanding of the approach, and identifying potential biases in the preference data.\n5. The inherent dependence between the policy used to train the reward model and the policy being learned is not addressed in the paper. For instance, in the single-agent setting (see [4]), this dependence can be significant. How does the proposed approach handle this issue?\n6. How does the quality of the learned reward function vary with different levels of expertise and sparsity in preference feedback?\n\n[1] Yu, Chao, et al. \"The surprising effectiveness of ppo in cooperative multi-agent games.\" Advances in Neural Information Processing Systems 35 (2022): 24611-24624.\n\n[2] De Witt, Christian Schroeder, et al. \"Is independent learning all you need in the starcraft multi-agent challenge?.\" arXiv preprint arXiv:2011.09533 (2020).\n\n[3] Rashid, Tabish, et al. \"Monotonic value function factorisation for deep multi-agent reinforcement learning.\" Journal of Machine Learning Research 21.178 (2020): 1-51.\n\n[4] Chakraborty, Souradip, et al. \"PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback.\" The Twelfth International Conference on Learning Representations." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "* This paper makes novel contributions to RLHF within multi-agent systems by framing the task as finding a Nash equilibrium in general-sum games and introducing innovative techniques for reward regularization and dataset distribution-based pessimism.\n* The theoretical results are comprehensive and well-justified, effectively supporting the paper’s claims.\n* The paper is generally well-written and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the important and timely problem of multi-agent reinforcement learning from human feedback (MARLHF). The authors examine both theoretical and practical aspects of MARLHF, demonstrating that single policy coverage is insufficient and emphasizing the need for unilateral dataset coverage. To address the issues of sparse and spiky reward learning typical in standard RLHF, they propose two primary techniques: (1) mean squared error regularization to promote uniform reward distribution, and (2) an additional reward term based on state-action pair density within the dataset to introduce pessimism, using an imitation learning-based approach for density modeling. The final policy is then trained using the VDN algorithm. Overall, this MARLHF approach represents a significant step toward preference-based reinforcement learning in multi-agent systems." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The empirical validation of the approach is limited, as the paper only includes experiments on three simple MPE environments. Since the authors utilized JAXMARL, testing on more realistic and complex environments from the JAXMARL API, such as Overcooked, Hanabi, or StarCraft, would strengthen the paper’s claims.\n* The comparison with MARL baselines is insufficient, focusing only on VDN despite its known limitations in representation capacity. Conducting ablation studies with other MARL algorithms, such as MAPPO[1], IPPO[2], and QMIX[3], would provide more validations." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "How would your approach need to be modified to handle inconsistent or non-transitive preferences that often occur with real human feedback?\nWhy do you call the paper MARLHF when there is clearly no HF?\nThe practical implementation differs significantly from the theoretical algorithm - can you explain this gap and discuss whether any theoretical guarantees carry over?\nGiven the relative simplicity of the tasks, why were only 5 random seeds used for the experiments?\nWhy weren't statistical significance tests performed to validate the comparative results?\nHow well does your approach scale with increasing numbers of agents? \nIn cases where mixed-skill policies outperform pure expert policies, can you verify that this reflects genuine improvement rather than issues with reward modeling?\nHave you tested MARL algorithms other than VDN?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "In terms of the proofs, there is a simple but convincing proof by counterexample provided for theorem 1 (not contradiction, as stated).\nThere is an explicit bounds found on the Nash-gap. \n\nHyperparameters used in the training are provided, multiple seeds are used and results that don’t support the desired conclusion are presented. Multiple environments are tested, and clear ablation studies are done.\n\nThe paper makes an interesting theoretical contribution by establishing fundamental results about Multi-Agent Reinforcement Learning from Human Feedback (MARLHF). The authors prove why single-policy coverage is insufficient and demonstrate that unilateral coverage is both necessary and sufficient for learning Nash equilibria. These theoretical foundations are presented with clear proofs that are well constructed. These theoretical results then explicitly inform the design of the framework which is clearly stated and explained.\n\nThe empirical work is comprehensive and well-designed, testing their approach across three distinct multi-agent scenarios that each present different challenges (cooperative target coverage, coordinated pursuit, and communication-dependent navigation). The experiments validate both the theoretical insights about dataset coverage and the effectiveness of their algorithmic innovations. Their ablation studies are thorough and give clear evidence for the value of their MSE regularization and dataset distribution-related penalties. The authors also introduce a practical standardization technique for hyperparameter tuning that works across different environments.\n\nThe clarity of the experimental setup makes the work also highly reproducible" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper addresses the problem of trying to learn human preferences (this behaviour is better than that behaviour) in a multi agent RL setup. In this case satisfactory learning means a Nash-equilibrium is reached between all policies. The authors positions the paper as an initial study into Multiagent Reinforcement Learning from Human Feedback.\n\nThe paper shows how pure expert policies are not always the best for maximising overall score, and that mixing in less expert policies in some cases causes an overall higher score to be reached in the MARLHF case. This is proved, theoretically. They also show that it is often easier to learn what policies score higher by having unilaterally divergent policies acting in the environment, where a single agent is using a sub-optimal policy. The authors call this approach unilateral coverage. By having this unilateral agent in the environment it becomes simpler to observe what policies may be truly optimal within the environment. In addition upper complexity bounds are established for Nash Equilibrium in effective MARLHF.\n\nThe process to implement this approach is to learn a reward function from a preference dataset while mitigating extrapolation errors with a pessimism term and then determining a final policy. Human Feedback is itself simulated using the Bradley-Terry-Luce model to rank solutions.\n\nThe authors make 2 particular contributions to implement their insights:\nApplying MSE regularisation to the training data to distribute rewards more evenly across timesteps, which helps to avoid temporal concentration. This essentially takes the sparse reward signals from the Bradley-Terry-Luce model and spread them out to produce reward over more timesteps.\nDataset distribution-based penalties are used to constrain exploration to known regions of the state space\n\nTheir empirical evaluation spans three multi-agent scenarios: cooperative target coverage, coordinated pursuit, and communication-dependent navigation. They show that incorporating imperfect policies is helpful for learning higher scoring policies during training. In harder tasks, unilateral coverage and diversity become more important and more diverse datasets led to lower variance in training outcomes. The authors also introduce a principled standardization technique for hyperparameter tuning across environments." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The main weakness is that despite the paper's title and framing, there is no actual human feedback involved in any of the experiments. Instead, the authors simulate preferences using the Bradley-Terry-Luce model based on known reward functions from the environments. This is a significant limitation because real human preferences are likely to be much noisier, inconsistent, and potentially non-transitive compared to their simulated preferences. The paper would be more accurately titled as \"Multi-Agent Reinforcement Learning from Simulated Preferences\" or similar, and should more explicitly acknowledge this limitation and discuss how their approach might need to be modified for real human feedback.\n\nWhile thorough, the theoretical results rely heavily on assumptions that may not hold in practice. The paper assumes linear Markov games and works with known feature mappings, but doesn't discuss enough how these assumptions might limit real-world applicability. Additionally, although the paper proves that their theoretical algorithm converges to Nash equilibria, the practical implementation uses different algorithms (VDN-based) with no theoretical guarantees. This gap between theory and practice is not sufficiently discussed. The paper also doesn't explore whether the Nash equilibrium is actually desirable in all cases - in some scenarios, other solution concepts might better align with human preferences. This again is one of the major weaknesses with the unclear framing.\n\nThe experimental evaluation, while systematic, is limited to relatively simple environments in the Multi-Agent Particle Environment (MPE) framework. These environments, while useful for testing basic concepts, are far simpler than real-world multi-agent scenarios. The paper doesn't adequately discuss how their approach might scale to more complex environments or to scenarios with larger numbers of agents. Their results showing that mixed-skill policies can outperform pure expert policies raise questions about whether their reward modeling approach is capturing the true objectives of the tasks. \n\nAnother important weakness in the paper's empirical evaluation is the absence of statistical significance testing. Although results with means and standard deviations across 5 random seeds are given, they don't perform any statistical analysis to validate the conclusions. This is particularly problematic given the small sample size - with only 5 seeds, the reliability of their comparisons is questionable. The paper lacks hypothesis tests. This makes it difficult to determine if the reported differences between approaches are statistically significant, especially in cases where the differences appear small relative to their standard deviations. For example, in Spread-v3, it's unclear whether the difference between \"Mix-Unilateral\" (-20.98 ± 0.56) and \"Mix-Expert\" (-21.11 ± 1.16) is meaningful. The lack of statistical rigor undermines the strength of the paper's empirical conclusions and the claims made about the benefits of their approaches." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. This paper analyzes the RLHF setting; however, the definition of the performance metric remains unchanged from the RL setting without KL regularization. Could the authors provide further clarification on this?\n\n2. Could the authors highlight the novel aspects of the current theoretical analysis that differentiate it from the offline MARL setting?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The theoretical analysis presented in this paper is solid and clear, providing a sound theoretical bound for the proposed method to solve MARLHF. Additionally, the authors conduct various experiments to demonstrate the effectiveness of the proposed method, even when applied to offline datasets lacking uniform coverage." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This study introduces Multi-Agent Reinforcement Learning from Human Feedback (MARLHF) to find Nash equilibria from preference-based data with sparse feedback. A key technique in this paper is to use the MSE regularization for uniform rewards and a pessimism-based penalty—to improve stability and performance, enabling more effective preference-based multi-agent systems." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The discussion section on related works is incomplete. The authors should provide a more thorough discussion of recent advancements in MARL and offline RLHF ([1]-[4]). Additionally, the paper emphasizes the importance of incorporating reward regularization in the objective function for the current task. However, similar ideas have been adopted in different contexts and should be discussed carefully ([3]-[6]).\n\n2. The current experiments primarily showcase different variants of the proposed methods and include an ablation study. Could the authors include more baseline methods for comparison? Additionally, incorporating more tasks (e.g., five tasks) would strengthen the findings and provide greater convincing power for readers.\n\n3. The theoretical analysis currently focuses solely on the linear function approximation setting, which may not be realistic given the use of neural networks in the experiments. Could the authors extend the analysis to accommodate general function approximations, or clarify how the experimental setup meets the requirements of linear function approximation?\n\n4. In Line 300, it seems that someone even left comments colored in blue, which may leak the information of the authors. It is suggested that the authors should double-check the submitted draft to avoid this careless mistake.\n\n5. In Line 276, the reference to \"an approximate Nash equilibrium policy\" in the theorem lacks clarity, as it does not illustrate the approximation error in relation to the size of the offline dataset. The authors should expand on the implications of the derived bound and compare their results with existing theoretical findings in the offline RL and MARL literature.\n\n\n[1] Wang, Yuanhao, et al. \"Breaking the curse of multiagency: Provably efficient decentralized multi-agent rl with function approximation.\" The Thirty Sixth Annual Conference on Learning Theory. PMLR, 2023.\n\n[2] Xiong, Nuoya, et al. \"Sample-Efficient Multi-Agent RL: An Optimization Perspective.\" The Twelfth International Conference on Learning Representations.\n\n[3] Liu, Zhihan, et al. \"Provably mitigating overoptimization in rlhf: Your sft loss is implicitly an adversarial regularizer.\" arXiv preprint arXiv:2405.16436 (2024).\n\n[4] Cen, Shicong, et al. \"Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF.\" arXiv preprint arXiv:2405.19320 (2024).\n\n[5] Mete, Akshay, et al. \"Reward biased maximum likelihood estimation for reinforcement learning.\" Learning for Dynamics and Control. PMLR, 2021.\n\n[6] Xie, Tengyang, et al. \"Bellman-consistent pessimism for offline reinforcement learning.\" Advances in neural information processing systems 34 (2021): 6683-6694." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Figure 1: $\\pi_{ref}$, while mentioned in the caption, doesn't seem to be appearing in the figure. Do you mean $\\pi_b$?\n- What does the blue text mean in Lines 300-301?\n- Table 2: The claim in the capture, namely, \"in more challenging environments, such as Tag-v3, dataset diversity plays a substantially more significant role\", seems inconsistent with the data in the table, where both the mean and the variance of the return of Tag-v3 reach their best in the Pure-Expert dataset which has the least diversity.\n- Table 2: The claim in Lines 419-420, namely, \"In more challenging tasks, as reflected by higher MSE, the importance of unilateral coverage and diversity becomes more pronounced.\", does not seem very obvious from the table, where the diversified and the mix-unilateral dataset achieve the best performance when (Spread-v3 for Mix-unilateral and Reference-v3 for Diversified) the corresponding MSE is low.\n- Table 3: Why does setting $\\beta$ to a magnitude as large as 100 yield such good results? Doesn't the penalty term completely dominate the loss? Further, it seems strange to me that setting $\\beta$ across such a wide range (from 1 to 100) can yield almost the same result, especially when the dataset is the diversified one which contains a large fraction of low-return trajectories.\n- Figure 2: What does the x-axis represent?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- I am not an expert in RLHF, but to my best knowledge, this is the first work for aligning multi-agent systems with human feedback.\n- The theoretical claims are concise and seems to be practically useful.\n- The experiments are well designed for the purpose of verifying the proposed theoretical claims and empirical techniques." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper seeks to establish theoretical foundations and make empirical validations for the new research field, Multi-Agent Reinforcement Learning from Human Feedback (MARLHF). The core theoretical contribution is proving that single-policy coverage is insufficient for learning approximate Nash equilibrium policies and that unilateral policy coverage is sufficient to do so. The empirical contribution lies in two techniques, namely, reward regularization which smoothens the reward distribution, and dataset distribution-based pessimism which handles the extrapolation errors. The experiments are designed to verify the correctness of the theoretical claims and the effectiveness of the empirical techniques." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The experiments are conducted on a limited range of tasks, which may not be sufficient to verify the generality of the theoretical claims and empirical techniques.\n\nAs far as I can tell, there are no other obvious weaknesses of this paper. Potential weaknesses concerning the consistency between the experiment results and the corresponding conclusions are listed as questions below." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We study Multi-Agent Reinforcement Learning from Human Feedback (MARLHF) with preference-only offline data." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024multiagent,\ntitle={Multi-Agent Reinforcement Learning from Human Feedback: Data Coverage and Algorithmic Techniques},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4vPC6Aj6N7},\nnote={under review}\n}" }, "abstract": { "value": "We initiate the study of Multi-Agent Reinforcement Learning from Human Feedback (MARLHF), exploring both theoretical foundations and empirical validations. We define the task as identifying Nash equilibrium from a preference-only offline dataset in general-sum games, a problem marked by the challenge of sparse feedback signals. Our theory establishes the upper complexity bounds for Nash Equilibrium in effective MARLHF, demonstrating that single-policy coverage is inadequate and highlighting the importance of unilateral dataset coverage. These theoretical insights are verified through comprehensive experiments. To enhance the practical performance, we further introduce two algorithmic techniques. \n(1) We propose a Mean Squared Error (MSE) regularization along the time axis to achieve a more uniform reward distribution and improve reward learning outcomes. \n(2) We propose an extra penalty based on dataset distribution to incorporate pessimism, enhancing stability and effectiveness during training.\nOur findings underscore the multifaceted approach required for MARLHF, paving the way for effective preference-based multi-agent systems." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "multi-agent reinforcement learning", "reinforcement learning with human feedback", "dataset coverage" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/0606799dd9e8bc840831f00ae37ad50960381867.pdf" }, "presentation": null, "primary_area": { "value": "reinforcement learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/ad5a85e9f3ecce7bed7cd27bb74d1a95a575ef9b.zip" }, "title": { "value": "Multi-Agent Reinforcement Learning from Human Feedback: Data Coverage and Algorithmic Techniques" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Why do you not include any temporal clustering methods (possibly after interpolation)? How do temporal clustering methods perform on this type of data?\nWhich of the methods in related work are applicable to your evaluation scenario? You could consider including a table that lists core properties, indicating which of them are met by which competitor, instead of (long) textual descriptions." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "S1. The paper addresses an important problem of handling missing values.\nS2. The empirical evaluation makes use of five different benchmarking data sets for motion segmentation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a method for clustering of sequential data with missing values. It incorporates temporal constraints to model the expected continuity across time. It characterises the method theoretically, and evaluates on different motion segmentation benchmark datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "W1. The presentation of the paper is weak, lacking discussion, which makes it hard to follow. Concretely, the scope of the paper is unclear initially, going from clustering of missing features to subspace clustering and back. After that, the method and its properties are presented, without discussing how it relates to existing work, what motivates each step, and whether there are design alternatives.\nW2. The discussion of related work fails to convey which methods are related in terms of applicability to the problem under study or in terms of method similarities, and which are less closely related. Instead, the related work section lists technical aspects of different methods, without any assessment as to their suitability for the problem under study.\nW3. The empirical study is weak. In the experimental evaluation, only methods for clustering with missing data are studied, but none for clustering temporal data. As the datasets under study are characterised by strong temporal signals, the competitors are thus very weak baselines.\nW4. The paper fails to provide sufficient information about the setup in the experiments, and some details about the method are confusing. For example, for the experiment in Fig. 2, linear interpolation is used prior to running the method. This seems to contradict the purpose of the method of being able to handle missing data. Also, it is unclear if linear interpolation was also used prior to running the competitors. This should be clarified in the description, and experiments comparing with and without interpolation should be conducted.\nW5. The accuracy in several of the experiments is very high, close to 80%, even when half of the data is missing - indicating that the problem might be too easy for any temporal method (as stated above, none of them are considered here). The experimental evaluation should thus include temporal clustering methods as well, possibly using interpolation if necessary (as in W4). More challenging datasets, where missing data has a stronger impact on accuracy should be studied in order to understand the robustness of the method.\nW6. It is unclear how quickly the method converges in general, only one example is provided for one of the datasets. The paper should provide convergence results across datasets and runs." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "(Q1) Why are the Figure 3 to Figure 6 only reported on a single dataset while ignoring the other 4 selected datasets? \n\n(Q2) Please introduce the data in a more structured way before section 3. A clearer definition should also be introduced to show what do you mean by ‘temporal semantics’ and why they are important, instead of talking about concept in a high-level way.\n\n(Q3) Please also include the description of temporal semantic in algorithm 1 to explicitly make people know how it works." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "(S1) The work proposes a novel angle to cluster human motion data considering data temporal sequence.\n(S2) It outperforms the previous baselines when adapted with the ‘missing entries’ data processing proposed by this work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduced a novel clustering framework called Exploring Temporal Semantic for Incomplete Clustering (ETC-IC), which leverages temporal information within sequential data to enhance the clustering accuracy. Unlike previous works, ETC-IC clusters data without requiring prior imputation, making the results less sensitive to missing data attribute issues. This work valid ETC-IC on 5 human motion benchmarks and the proposed model consistently surpasses current SOTA methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(W1) Experiment -- The experiments are not sufficiently well designed. Firstly, they adapt the proposed MAR and MNAR directly to the previous baselines without comparing the baselines’ original performances. Secondly, one of the core concept in this paper: missing entries, are manually designed and generated rather than an off-the-shelf nature existing in the dataset. The results should also show how the model behaves on samples without missing entries to show the model’s general capacity. Thirdly, the quantitative analysis is poor, where Figure 5 only showcases some samples while Figure 7 is a case study rather than quantitative analysis.\n\n(W2) Presentation -- Moreover, the presentation of the experiments is with many errors. Firstly, both Figure 1 and Figure 8 report results on 5 datasets, but the figure caption and paper introduce there are only 4 datasets instead of 5. Secondly, the introduction to Mocap is poor, where it doesn’t introduce clearly what are the sequence data. Thirdly, Fig 2 (b) and Fig 3 (b) are with low quality. There is a clear obstruction between plot lines and bottom-left frames. Fourthly, the ablation study is reported in a Table but presented as a Figure (Figure 8). Also, the caption is on top of figure 8 while all the other captions for Figures stay in the bottom. \n\n(W3) Lack of guidance -- Besides the experiments, it’s also very hard to comprehend the authors formal derivation, as there are very few intuitive explanations. Without a clear guidance and introduction to the data, the author directly introduces the formulas without elaborating how the symbols are connected to the data in this work. Later on, in the experiment, only a simple ablation study in Figure 8 shows the effectiveness of ‘temporal semantics’, while it doesn’t analyze the effectiveness of theorem1 to 4 respectively, leaving it unknown which part of the theorem truly works, and which part might fail. Also, the theorem 3 is missing, where it is replaced by ‘proposition 3’." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Is the objective function convex, and if so, suggest adding a proof of convergence analysis instead of just giving a figure 6?\n2. Did the authors use five datasets or four? Why is it four at one time and five at another?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper presents a clustering framework distinguished by its remarkable adaptability in addressing the inherent challenges posed by incomplete sequential data.\n2. We introduce an innovative temporal semantic consistency constraint, which markedly enhances the efficacy of subspace clustering for sequential data.\n3. We provide a rigorous theoretical analysis, enabling an equivalent approach even in the presence of missing data, whilst effectively exploring temporal semantics." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces ETC-IC framework, which possesses the capability to seamlessly integrate temporal information while concurrently addressing the challenge of missing data. Firstly, to manage the issue of missing entries, ETC-IC employs an algebraic subspace analysis and develop a theoretically grounded alternative, thereby ensuring accurate clustering even in the presence of incomplete data. Secondly, ETC-IC explores the temporal semantics inherent in sequential data by aligning data points and their temporal assignments through a temporal semantic consistency constraint, thereby ensuring that data points with similar temporal semantics are clustered together. The handling of missing data and the exploration of temporal semantics are unified within a single cohesive framework, thereby demonstrating the adaptability and versatility of the proposed method in addressing incomplete sequential data as required." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In this paper, while the algorithm has already been exhaustively described and experimentally validated, it is recommended to include an analysis of the algorithm's time complexity to further enhance the completeness.\n2. it is recommended to incorporate additional evaluation metrics to further strengthen the assessment of its performance." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1.\tIt is recommended to provide a summary of the entire work through a framework figure.\n2.\tThe model is evaluated solely on the human motion dataset, and thus this work is validated within the human motion domain. Consequently, the current title is not appropriate, authors should revise it to ‘human motion learning’ or evaluate this model in a broader context.\n3.\tIn the ABLATION STUDY section, the authors should provide a detailed explanation of the ablation experiment setup and non-temporal semantics to validate the module's effectiveness." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This paper is well written." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Comments:\nThe manuscript presents a novel clustering framework, ETC-IC, intended to tackle the issue of clustering sequential data with incomplete features. The topic is of significant contemporary relevance, given the increasing focus on data with missing attributes in the clustering domain.\n\nWeaknesses:\n1.\tIt is recommended to provide a summary of the entire work through a framework figure.\n2.\tThe model is evaluated solely on the human motion dataset, and thus this work is validated within the human motion domain. Consequently, the current title is not appropriate, authors should revise it to ‘human motion learning’ or evaluate this model in a broader context.\n3.\tIn the ABLATION STUDY section, the authors should provide a detailed explanation of the ablation experiment setup and non-temporal semantics to validate the module's effectiveness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Weaknesses:\n1.\tIt is recommended to provide a summary of the entire work through a framework figure.\n2.\tThe model is evaluated solely on the human motion dataset, and thus this work is validated within the human motion domain. Consequently, the current title is not appropriate, authors should revise it to ‘human motion learning’ or evaluate this model in a broader context.\n3.\tIn the ABLATION STUDY section, the authors should provide a detailed explanation of the ablation experiment setup and non-temporal semantics to validate the module's effectiveness." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024exploring,\ntitle={Exploring Temporal Semantic for Incomplete Clustering},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4vm6Nn2DW9},\nnote={under review}\n}" }, "abstract": { "value": "Clustering data with incomplete features has garnered considerable scholarly attention; however, the specific challenge of clustering sequential data with missing attributes remains largely under-explored. Conventional heuristic methods generally address this issue by first imputing the missing features, thereby making the clustering results heavily reliant on the quality of imputation. In this paper, we introduce a novel clustering framework, termed ETC-IC, which directly clusters incomplete data with rigorous theoretical guarantees, whilst concurrently leveraging temporal semantic consistency to enhance clustering performance. Empirical evaluations demonstrate that the proposed model consistently surpasses current state-of-the-art methods in clustering human motion data." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Temporal semantic", "incomplete clustering", "human motion segmentation" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/d407c1ecb06074b35dc5a8a2ce58fe4a1a68a4bc.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Exploring Temporal Semantic for Incomplete Clustering" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. The main setup is quite confusing to me. The paper first states that \"$f_{weak} \\in \\mathbb{R}^d$\" is the object we learn. Normally, the model is a function, not a vector, so this was not immediately clear. It is defined later in line 347 how we learn $ f $, which is quite far from where it was introduced (line 184). It would be better to define that we train $f$ by MNI earlier.\n\n2. In line 201, it says, \"As a consequence of our main results in Section 3, we will show that the above desiderata are achievable in a simple toy model; see Theorem 3.3 for a formal statement.\" However, Theorem 3.3 only considers desiderata 1.2 and 2.1, not the entirety of the desiderata.\n\n3. What is \"$t$\" in Equation (3) of Theorem 3.1?\n\n4. The notation $ u, p, q, r $ used is not very intuitive, and it makes the result difficult to interpret. Is there a simpler way to rephrase the result?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The math appears correct to me; the problem is significant, and desiderata 1 and desiderata 2 make sense." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper investigates weak-to-strong generalization in the setting of an overparameterized spiked covariance model with Gaussian covariates. The paper identifies an asymptotic phase transition between successful and unsuccessful generalization." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper is rather technical, and the clarity could be improved significantly to make it more readable. (see questions)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Suggestions:\n\n1. Reduce the introduction - it currently spans 2 pages. \n2. Figure 1 is useless.\n3. The section on data model was not particularly needed. Page 5 and 6 can be compressed into 1 or 2 paragraphs.\n4. Include some experiments in main body.\n\nIn general the paper is quite verbose, it can be compressed substantially and content moved back into main body." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Exact characterization of the regime where weak to strong generalization occurs in terms of parameters of the covariance matrix of strong and weak features." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The papers identifies a specific setting under which weak to strong generalization occurs. Consider a strong model that learns a classifier on strong features of the data by supervised learning on $m$ weak labels given by a weak model that was trained on weak features on $n$ clean labels. Then weak to strong generalization implies that \n\nCondition 1): The strong model has perfect classification accuracy whereas the weak model has close to random accuracy. \n\nCondition 2): The generalization is due to weak labels, i.e. if the strong model was only trained on $n$ clean labels, there is no generalization.\n \nThe setting is as follows: A learner observes features distributed according to a Gaussian distribution, $x \\sim N(0, \\Lambda)$ where $\\Lambda$ is diagonal covariance matrix following a bilevel ensemble parameterization \n\\begin{equation}\\lambda_j = \\lambda_F = \\frac{ad}{s} \\text{ for } 1 \\leq j \\leq s \\text{ otherwise } \\lambda_j = \\lambda_U = \\frac{(1-a)d}{d-s}\\end{equation}\nwhere $d = n^p, s= n^r, a = n^{-q}$ and $p > 1; q, r >0; q+ r < p$. For multiclass setting, classes are further scaled as $k = c_k n^t$ for some $t<r$. The strong model observes features given by some $p, q, r$ and weak model observes features characterized through $p_{weak}, q_{weak}, r_{weak}$. In particular the strong features $x_{strong}$ and weak features $x_{weak}$ are given as \n$$ x_{strong} = N(0, \\lambda_F I_{[s]} + \\Lambda_U I_{[d]/[s]}) $$\n$$ x_{weak} = N(0, \\lambda_{F, weak} \\Pi_S + \\Lambda_{U, weak} \\Pi_T)$$\nfor some subsets $S \\subseteq [s], T \\subseteq [d]/[s]$ and $\\Pi_S$ denotes projection onto axis aligned subspace indexed by $S$. $\\lambda_{F, weak} = \\frac{a_{weak}d_{weak}}{s_{weak}}$ and $\\Lambda_{U, weak} = \\frac{(1-a_{weak})d_{weak}}{d_{weak}-s_{weak}}$.\n\n The true labels are given by $y = \\text{sign}(x_1)$ for binary classification and $y = \\arg\\max_k (x_1, \\dots x_K)$ for $K$ way classification. \n\n\n \nIn this parameterized setting, the authors show that there is a particular regime of number of weak labels $m$ provided by the weak model (for certain regimes of $p, q, r, p_{weak}, q_{weak}, r_{weak}$) where weak to strong generalization occurs (condition 1) holds). The conditions (for binary classification) are given by (assuming $m = n^u$)\n\n1. $u + \\min(1 -r, p + 1 - 2(q + r)) > q_{weak}+r_{weak} > (p_{weak} + 1)/ 2$\n2. $p + 1 > (q + r + q_{weak} + r_{weak})$\n3. $u < (p + 1 + q + r - (q_{weak} + r_{weak})/ 2)$ \n\nFurther the classification error of strong learner trained on $n$ cleaned labels is shown to be depend as \n$$1/2 - 1/\\pi \\arctan (\\Theta(n^{p+1 - 2(q+r)}))$$\n\nThus they claim one can identify regimes under which condition 2) also holds (possibly when $p+1 - 2(q+r) << 1$) although no details are provided).\n\nFurther they provide an informal claim and details in appendix that there exists some regime for multi class setting." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Most of the important details are pushed into appendix. The main body only contains one useful theorem which identifies a certain condition where condition 1) of weak to strong generalization holds. Setting for condition 2) and multi class settings are merely mentioned as claims. The main body also does not provide proof sketch or provide insights into the proof of the theorem." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "21) What the the word \"represent\" mean in Desiredata 1.(ii).\n2) What is the significance of the bi-level-ensemble? \n3) What is $t$ in Theorem 3.1?\n4) Is there a reason for choosing a halfspace for the ground truth? Does this analysis extend to other concepts. Is there a similar notion for regression (rather than classification)?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1) This work addresses the important problem of obtaining theoretical justification for a frequently encountered empirical phenomenon\n2) The lower tail for max of correlated gaussians is an interesting result." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this work, the authors provide theretical justification for the empirically observed phenomenon of weak to strong generalization. In this setting, a weak learner is used to created labelled examples (from unlabelled training data) that is used to further train a stronger model. The intuition is that the weak learner has learnt some useful information about the ground truth and hence the pseudolabels it generates will actually enable generalization. The authors prove that this weak to strong generalization has two phases: (1) when the number of pseudolabelled examples is less than some threshold, the strong learner behaves like a random guesser, (2) beyond the threshold the strong learner achieves perfect generalization. A technically interesting tool that they use is a new lower tail for the max of correlated gaussians which could be of independent interest." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "See questions." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See weaknesses above. It would be nice if the authors can address these." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper studies a phenomenon that has been empirically observed and thus relevant to practice\n- The results and proof techniques seem non-trivial and interesting" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies a toy-model for weak-to-strong generalization. They show that under the assumptions of their toy-model, two asymptotic phases occur for the student: (1) it is able to successfully generalize or (2) the student resorts to effectively random guessing. The authors also try to extend their results to weak-to-strong multiclass classification and derive new lower tail inequalities for the max of correlated gaussians." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I find the organization and presentation a bit confusing and hard to parse. In particular, Theorem 3.3 is hard to interpret without referencing the Desiradatum outlined in Section 2. Ideally, Theorem 3.3 should be standalone and at the very least, the variables in Theorem 3.3 like $\\tau_{weak}, p_{weak}, ...$ should be defined. In addition, and in my opinion, the notation and current presentation of the result doesn't really make it seem like this is a \"simple, toy model\", given how many free variables there are to keep track of. One possible fix is to give more intuition and less notation about the toy-model in the main text, and push the details into the Appendix. For example, I think it would be really helpful to have an informal, non-rigorous theorem summarizing the main result in the Main Contributions section.\n\nIn addition, I am not sure what to take away from this paper. It is nice that you found a toy example, where you can provide rigorous evidence of the empirical phenomena of weak-to-strong generalization. However, I am not convinced this toy model is realistic/relevant to practice, even after reading the Modeling assumptions in the Discussion. In short, it would be nice if the authors can answer:\n- **why** one should care about finding a \"simple, concrete theoretical setting where we can provably exhibit different phases of weak-to-strong generalization?\" \n- what can I take away from this result?" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We use recent advances in benign overfitting for classification to prove weak-to-strong generalization in a toy setting." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024provable,\ntitle={Provable weak-to-strong generalization via benign overfitting},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4vzGQcVUG8},\nnote={under review}\n}" }, "abstract": { "value": "The classic teacher-student model in machine learning posits that a strong teacher supervises a weak student to improve the student's capabilities.\n We instead consider the inverted situation, where a weak teacher supervises a strong student with imperfect pseudolabels. \n This paradigm was recently brought forth by \\citet{burns2023weak} and termed \\emph{weak-to-strong generalization}. \n We theoretically investigate weak-to-strong generalization for binary and multilabel classification in a stylized overparameterized spiked covariance model with Gaussian covariates where the weak teacher's pseudolabels are asymptotically like random guessing.\n Under these assumptions, we provably identify two asymptotic phases of the strong student's generalization after weak supervision: (1) successful generalization and (2) random guessing. \n Our techniques should eventually extend to weak-to-strong multiclass classification. \n Towards doing so, we prove a tight lower tail inequality for the maximum of correlated Gaussians, which may be of independent interest.\n Understanding the multilabel setting reinforces the value of using logits for weak supervision when they are available." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "benign overfitting", "spiked covariance models", "overparameterized models", "interpolation", "pseudolabeling", "weak-to-strong generalization", "alignment" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/e0e9c3071424318e0cf247d02889aac6e0842694.pdf" }, "presentation": null, "primary_area": { "value": "learning theory" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Provable weak-to-strong generalization via benign overfitting" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see the weakness." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This work claims to be the first work to introduce a reward-controlled framework in the concept composition generation, which is somehow novel in this field.\n2. This work is presented well with complete theoretical details and proof.\n3. The quantitative experimental results show better performance compared to SOTAs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes IterComp, an iterative composition-aware reward-controlled framework. It introduces a model gallery and constructs a high-quality composition-aware model preference dataset. Utilizing a new iterative feedback learning framework, IterComp progressively enhances both the reward models and the base diffusion model." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The qualitative comparison in Fig. 4 is confused. There seems to be a marginal improvement in Line 3 and Line 4. The authors should make the difference between the qualitative examples clear to be recognized.\n2. The qualitative examples are not enough to evaluate the model performance since the cases in the paper are somehow complex. The authors can provide more examples for a single case. Also, there is a need to evaluate the stability of the proposed model.\n3. The comparison with InstanceDiffusion is confusing. As a layout-guided method, InstanceDiffusion needs detailed layout inputs. It is not fair to provide only one box if the case includes two or more instances, as indicated in the third line of Fig. 4. As the authors attempt to compare with layout-to-image methods, a SOTA method named MIGC [1] is also not included.\n\n[1] Zhou, Dewei, et al. \"Migc: Multi-instance generation controller for text-to-image synthesis.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See weakness" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Novel and practical approach: The paper presents a simple yet effective way to combine multiple models' strengths for compositional generation without increasing computational complexity.\n\nStrong empirical results: The method shows clear improvements over existing approaches, with comprehensive evaluations on both compositional accuracy and image quality metrics.\n\nWell-structured technical contribution: The paper provides clear theoretical analysis with detailed proofs, and the iterative feedback learning framework is well-designed and easy to implement." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces IterComp, a novel framework that enhances compositional text-to-image generation by aggregating preferences from multiple diffusion models through iterative feedback learning. The approach demonstrates superior performance in both compositional accuracy and image quality, while maintaining efficient inference speed. The main strength lies in its ability to combine different models' advantages without adding computational overhead, though the long-term stability of the iterative learning process could be further explored." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper mentioned that RPG is challenging to achieve precise generation, but Tab2 did not compare with RPG, and I checked that RPG's performance on T2I-Compbench is better than that of the paper.\n\nIt is necessary to test the results of FLUX-dev directly on the t2i compbench to see how much improvement the method proposed in this paper has. I currently suspect that the improvement may not be very significant." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. As mentioned in W.1, how long does the training loop take (including the iterative feedback learning)?\n2. Could I use this method to improve a specific concept generation (e.g., a human-object interaction)? How much time does it take from collecting synthetic data to finalizing the model training?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper presents a novel framework combining preferences from multiple diffusion models to enhance compositional text-to-image generation and address the relationship understanding in diffusion models.\n2. The qualitative/quantitative results show comparable improvements in compositionality.\n3. A composition-aware dataset is collected which provide diverse preferences that inform the reward models. (will it be released in the future?)" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper propose a framework that aggregates composition-aware model preferences from multiple models and employs an iterative feedback learning approach to enhance T2I compositionality and general performance. The qualitative and quantitative results show their SOTA compositional generation capabilities compared to previous works." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. There is limited discussion of the computation resources required to manage multiple reward models, which may affect the scalability in large-scale applications. Although the authors claim that their model has fast inference speed, the cost of model training and data collection is not clear. This makes me feel less likely than DPO to be widely used in practice.\n2. The user study only demonstrates the user preferences lacking the deep analysis of attribute binding and object relationship, which are critical to model performance. 16 samples is also too small to evaluate such a complex task." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "In addition to the weakness mentioned above, I have a question regarding stability. \n\nBased on my experience, RLHF in diffusion models can often be unstable. I’m curious whether your method consistently produces stable results or if there’s a risk of occasionally poorer outcomes. I’m concerned that the iterative training process might lead to overfitting on biases present in the reward models, potentially reducing overall robustness.\n\nI hope the authors can make up for the weaknesses mentioned and address these questions." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper is well-structured, making it accessible and easy for readers to follow.\n2. Clear formulas are provided for each component, effectively removing any ambiguity.\n3. Mathematical proofs substantiate the validity of the proposed method.\n4. The authors conduct detailed, extensive experiments to support their approach.\n5. Illustrative images are included, enhancing clarity and understanding." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This study addresses improving the compositional text-to-image generation capability of less powerful diffusion models. The authors contribute in two main areas. First, they decompose the capabilities of various diffusion models into three domains—attribute binding, spatial relationships, and non-spatial relationships—and rank model outputs accordingly to guide reinforcement learning with reward models specific to each domain. Second, they introduce an iterative training process that utilizes the fine-tuned diffusion model outputs to progressively enhance the reward models.\n\nThrough multiple experiments, the study demonstrates the proposed method’s effectiveness, enabling early-stage models to achieve comparable generative abilities with reduced inference time. The authors also verify the effectiveness and general applicability of each design component across different models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. About the number of texts used for attribute binding, \"500 prompts\" in Line 185 is inconsistent with \"1500\" in Table 1. Which is correct?\n2. Although the experiments are detailed, some comparisons appear incomplete. The reinforcement learning from human feedback (RLHF) approach leverages outputs from advanced models like FLUX and SD3 for training, yet direct comparisons with these models are not provided. Including these comparisons would better highlight the method's effectiveness.\n3. An additional experiment focusing on the first-round fine-tuned model—trained solely with human-annotated data—would be valuable. This would clarify the necessity and impact of the iterative training approach." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "* Training time considerations:\n - Diffusion-DPO operates in the latent space without requiring image decoding. In contrast, IterComp requires image decoding for the reward models (though this could be avoided by training the reward model with latent space inputs), likely resulting in slower training. Additional commentary on the training scheme would be valuable.\n* Potential code and paper discrepancy:\n - The paper describes an iterative feedback mechanism that optimizes both reward models and the base model. However, examination of `feedback_train.py` reveals that only unet parameters (base model) are passed to the optimizer. This suggests that only the base model is being optimized, while reward models remain static. This difference requires clarification.\n* Question regarding test-time adaptation:\n - Could the iterative feedback mechanism be applied as test-time adaptation of the base model? Similar to Slot-TTA [1], the base model could be optimized using reward models to improve compositional quality. The process would work as follows: for a given prompt, the base model generates an image, which is then evaluated by reward models. The base model's parameters would be updated to maximize these rewards. This process could be repeated for several iterations. This approach would eliminate the need for training the base model, allowing it to adapt to any prompt at test time through multiple iterations. Comments on this possibility would be valuable.\n\n[1] Test-time Adaptation with Slot-Centric Models, Prabhudesai et al., ICML 2023" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* The collected dataset can lead to better models and new research directions. Furthermore, researchers can follow a similar approach to collect their own data. The data can also be used for other RLHF methods, such as diffusion-DPO.\n* The iterative feedback mechanism seems to be a novel way to optimize both reward models and the base model.\n* The code is shared, which allows reviewers to follow the details of the proposed method.\n* The performance gain from IterComp appears significant, as evaluated through user studies, quantitative analysis, and qualitative assessment." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a new dataset for compositional generation where experts evaluate the outputs of multiple pre-trained models. The dataset consists of three aspects of compositionality: attribute binding, spatial relationships, and non-spatial relationships, along with 52.5K image-rank pairs, which can be used for training feedback, ranking, or reward models.\n\nThe second contribution of the paper is using the collected dataset to train reward models for each of the three key aspects. A multimodal model (BLIP) is used as a feature extractor for both the prompts and the generated images, and the extracted features are projected by MLPs to output the reward. The goal is to predict how good the given image-prompt pairs are by training the model similar to contrastive learning (moving toward winning examples and away from losing examples).\n\nThe third contribution is improving the compositional ability of a base model (SDXL is selected but it should not be limited to that) by optimizing it using the trained reward models. The base model is trained to maximize the reward model's output so that its outputs are better aligned with the reward models, which are trying to enforce compositionality. Furthermore, the paper proposes an iterative update mechanism for both reward models and the base model. Reward models are updated to predict the rankings generated by experts while the base model is updated to maximize the outputs of reward models. Through this process, both the base model and reward models are improved for their specific tasks.\n\n**Important note:** This review's technical content and analysis are my original work. Large Language Models were used solely to improve grammar and writing style, without altering any meanings or substantive feedback." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* Table 5 should be in the main part instead of the Appendix, as it simply demonstrates that the proposed method outperforms previous methods.\n* Several experiments are missing:\n - The paper combines all reward models simultaneously, likely leading to improved compositional performance. However, reviewers would benefit from seeing the individual effect of each reward model.\n - While SDXL is chosen as the base model, testing other models would help reviewers understand how reward models affect different base architectures.\n - It would be valuable to examine Diffusion-DPO's performance when trained on the collected dataset. Currently, Diffusion-DPO is trained on the pick-a-pic dataset, which is larger but lacks compositional details. These results would be necessary to evaluate the proposed method's effectiveness using consistent standards.\n* Expert ranking may inadvertently include aesthetic information [This observation is meant to prompt discussion rather than highlight a weakness, as the underlying cause remains unclear]:\n - When IterComp is applied, the aesthetic score improves, suggesting that reward models can interpret image aesthetics, since reward maximization leads to better aesthetic scores. This could be because either expert rankings are influenced by image aesthetics (beyond compositional attributes) or because models with better composition naturally generate more aesthetic images (for instance, FLUX, being the best compositional model, likely produces more aesthetically pleasing outputs).\n* Some experimental conditions may be misleading:\n - The paper uses 40 inference steps for all models to ensure fairness. However, some models can generate samples with fewer steps; for example, FLUX-dev uses 28 steps by default." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024itercomp,\ntitle={IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4w99NAikOE},\nnote={under review}\n}" }, "abstract": { "value": "Advanced diffusion models like Stable Diffusion 3, Omost, and FLUX have made notable strides in compositional text-to-image generation. However, these methods typically exhibit distinct strengths for compositional generation, with some excelling in handling attribute binding and others in spatial relationships. This disparity highlights the need for an approach that can leverage the complementary strengths of various models to comprehensively improve the composition capability. To this end, we introduce IterComp, a novel framework that aggregates composition-aware model preferences from multiple models and employs an iterative feedback learning approach to enhance compositional generation. Specifically, we curate a gallery of six powerful open-source diffusion models and evaluate their three key compositional metrics: attribute binding, spatial relationships, and non-spatial relationships. Based on these metrics, we develop a composition-aware model preference dataset comprising numerous image-rank pairs to train composition-aware reward models. Then, we propose an iterative feedback learning method to enhance compositionality in a closed-loop manner, enabling the progressive self-refinement of both the base diffusion model and reward models over multiple iterations. Detailed theoretical proof demonstrates the effectiveness of this method. Extensive experiments demonstrate our significant superiority over previous methods, particularly in multi-category object composition and complex semantic alignment. IterComp opens new research avenues in reward feedback learning for diffusion models and compositional generation." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Compositional text-to-image generation", "Feedback learning for diffusion model" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/fcfed353ef28cebc24d7813167dfea80074dab09.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/7791136c5f27b8ad1a58d37e5da5cb8c43370cef.zip" }, "title": { "value": "IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "Please refer to the weaknesses above." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Writing quality is good. The paper is well-structured, and clearly written. \n2. Good insights. This paper explores TTA as a domain generalization problem, uncovering linear connectivity within TTA models. This perspective suggests that domain generalization techniques could enhance model representations for TTA tasks. \n3. SOTA performance. The proposed method achieves the state-of-the-art performance via the integration with different TTA models in various scenarios.\n4. Ablations. Ablation experiments are provided to verify the effectiveness of the proposed modules." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a novel test-time ensemble approach that can be seamlessly integrated with existing TTA models to enhance adaptation. Specifically, the proposed framework reduces domain gaps through two ensemble strategies: weight averaging of TTA models and dropout. Additionally, a knowledge distillation strategy is employed to mitigate both noise and bias for improving model robustness under different distribution shifts. \nExtensive experiments are conducted in different TTA scenarios to demonstrate the superiority of the proposed method over existing baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Although the results presented in Tables 3 show the performance improvement achieved by the proposed framework in the continual TTA scenario, it is unclear how the method enhances baseline performance in later adaptation stages. Additionally, I would like to know if the proposed method addresses the issue of catastrophic forgetting in this context. \n\nAdditional question:\nIs it possible to extend the proposed benchmark construction method to dense prediction tasks, such as semantic segmentation? It would be very meaningful if it can be applied to various tasks." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Why the results of CoTTA in Continual TTA with non-i.i.d. conditions are only 2.2% and 3.4%?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. TTE utilizes an ensemble strategy to dynamically enrich model representations during online test-time adaptation (TTA), which is an interesting approach.\n2. TTE constructs an ensemble network by averaging the parameter weights of different TTA models, and this weight averaging captures model diversity, improving representation quality without increasing the computational burden of managing multiple models.\n3. TTE further promotes the diversity of representations within TTA models by combining with dropout, and proposes a debiased and noise-resistant knowledge distillation scheme to stabilize the learning of TTA models in the ensemble.\n4. The experiments are extensive and the results are superior to other compared methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a method called Test-Time Ensemble (TTE), which uses an ensemble strategy to dynamically enrich model representations during online test-time adaptation (TTA). TTE constructs an ensemble network by averaging the parameter weights of different TTA models, which are continuously updated using test data. This weight averaging technology captures model diversity and improves representation quality without increasing the computational burden of managing multiple models. TTE further combines dropout to promote diverse collaboration of representations within TTA models, and also proposes a debiased and noise-resistant knowledge distillation scheme to stabilize the learning of TTA models in the ensemble. TTE can be seamlessly integrated with existing TTA methods, enhancing their adaptive capabilities in various challenging scenarios." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The TTE method involves multiple hyperparameters, such as the momentum coefficient , dropout ratio and temperature, which may affect the stability and generalization of the method. Further research on how to reduce the dependence on hyperparameters is crucial.\n2. TTE integrates many well-established techniques such as ensemble, dropout, knowledge distillation, which have been utilized in TTA or few shot learning. The combination has weaken the novelty of the paper, and the unique contributions of the paper should be classified.\n3. How to conduct the weight-space ensemble without adding computational complexity? Will the technique increase storage consumption?\n4. The knowledge distillation-based debiasing and anti-noise strategies proposed in the paper may not be able to completely solve the problem of noisy test data. How to solve the scenario that the pseudo labels are incorrect?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "Here, I am adding the questions that I find relevant for improving the work quality; some of them were already discussed in the Weaknesses section:\n\n- Could you clarify this a bit more in section 3.2? How is it important for the method?\n\n- Do you think the batch size can impact the results, especially the ones provided for the continual TTA?\n\nPlease consider answering the points on the weaknesses as well. Furthermore, I am open to discussion, and I think that the work has a good potential for the community." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The paper is well-written, easy to follow, and detailed. I liked how the authors presented the work and motivated toward the problem. Furthermore, the results are motivating, and the idea seems easy to implement on top of different methods (as demonstrated by the authors), which can be beneficial for the community if the authors also provide the full code for reproducibility." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces the Test-Time Ensemble (TTE), a method designed for TTA using the theory of weights space ensemble, which can be used on top of different TTA methods. The authors show different results for TTA over corruptions with different baselines, and the method seems to work pretty well. Furthermore, the authors also provided results for continual TTA, which is interesting." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Personally, I did not see many problems with the paper, but I would suggest the authors proofread again to avoid problems such as the following typo \"with lager and more complex\" -> \"with larger and more complex\" in the introduction or \"Adaptvie momentum\" -> \"Adaptive momentum.\" \n\nIf the authors work on the following points, it will improve a lot the quality of the work: \n- I am not so convinced by section 3.2, DE-BIASED AND NOISE-ROBUST KNOWLEDGE DISTILLATION. Could you clarify this a bit more in this section? And maybe make it more clear in the paper.\n\n- In Equation 5, there is no hyperparameter to balance the terms. I think it should be included, right? \n\n- For Table 3, with continual TTA, the authors compared all other baselines with TTE with DeYO, but not TTE with other variants as well, and for continual TTA, I don't understand how the performance can still perform well in the direction of the adaptation without degrading too much as we can see in the other approaches (for example DeYO goes from 28.1 to 3.7 and then 7.2), for me it only make sense if you ensemble with zero-shot model as well (or reset the model weights) but if this is the case it should be done for all other baselines as well.\n \n- For Table 4, column V2 seems strange, as almost all results in a) are 68.9, even DeYO and DeYO with TTE. Could you also add more results with other batch sizes? (the batch size can play an important role in different algorithms, which can benefit DeYO and not the others. For instance, I recommend taking a look at the paper \"Bag of Tricks for Fully Test-Time Adaptation, IEEE/CVF Winter Conference on Applications of Computer Vision. 2024\", which shows the role of batch size in some of the TTA algorithms.\n\n- I would suggest revisiting some of the baselines for Tab 1. I would also consider a baseline with other methods of the local ensemble as well, such as SWA with TTA, and for Tab 4. I would also add other methods with TTE (maybe in the supp. material)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see the weakness." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "(1) This paper proposes the new problem, test-time problem, which is different from previous test-time adaptations. I believe this problem has some practical applications.\n\n(2) The paper proposes some simple baseline methods that can effectively address the problem." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper considers a new problem, test-time ensemble (TTE), which aims at using multiple models generated during TTA. This paper first formulates the test-time ensemble problem. The paper also proposes the weight average and dropout as the baseline methods to evaluate the performances. \n\nThe contributions can be summarized as: \n\n(1) The author revealed that TTA models exhibit linear mode connectivity, an in- triguing insight that simplifies and enhances the adaptation process.\n\n(2) The author introduced Test-Time Ensemble (TTE), a novel and computationally efficient approach that not only enriches model representations but also stabilize TTA optimization through de-biased and noise- robust knowledge distillation.\n\n(3) TTE integrated effortlessly with existing TTA methods, enhancing adaptation in diverse scenarios and showing potential for applicability to future TTA methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(1) The analysis of test-time adaptation does not inspire the new methods. The moving average ensemble methods are popularly adapted in self-supervised learning and ensemble methods. I consider the Linear Mode Connectivity theory should tell the reason and the situation that the models generated during test-time adaptation. \n\n\n(2) Limited technical novelty: this paper proposes the two-branch structure and leverage the weight average to improve the performance. Similar techniques are implemented in https://github.com/huggingface/pytorch-image-models. I do not see anything new compared to what have been proved in image classification.\n\n(3) Unclear description. In section 3, the de-biased distillation subsection does not describe clearly where the bias comes from. I suggest the author should explain the bias again. Also, I can not understand what the connection between the spike phenomena of the accuracy curve and the bias." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Leveraging linear mode connectivity to enhance test-time adaptation through ensemble methods." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024testtime,\ntitle={Test-Time Ensemble via Linear Mode Connectivity: A Path to Better Adaptation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4wk2eOKGvh},\nnote={under review}\n}" }, "abstract": { "value": "Test-time adaptation is a valuable approach for online adjustment of pretrained models to handle distribution shifts in test data. While existing research has focused primarily on optimizing stability during adaptation with dynamic data streams, less attention has been given to enhancing model representations for improved adaptation capability. This paper addresses this gap by introducing Test-Time Ensemble (TTE), which leverages two key ensemble strategies: 1) averaging the parameter weights of assorted test-time adapted models and 2) incorporating dropout to further promote representation diversity. These strategies encapsulate model diversity into a single model, avoiding computational burden associated with managing multiple models. Besides, we propose a robust knowledge distillation scheme to prevent collapse during adaptation, ensuring stable optimization. Notably, TTE integrates seamlessly with existing TTA approaches, advancing their adaptation capabilities. In extensive experiments, integration with TTE consistently outperformed baseline models across various challenging scenarios, demonstrating its effectiveness and general applicability." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "test-time adaptation", "domain adaptation", "linear mode connectivity" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/e4cc84ce42f742d5f02b7bc5bbbcd737a4198da0.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Test-Time Ensemble via Linear Mode Connectivity: A Path to Better Adaptation" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "* When are the conditions in Definition 1 expected to hold? Examples of suitable utility functions in binary classification and scalar regression can be very helpful.\n* Could notation in eq. (1) be clarified? Specifically, $Y_{H_{t-1}}$ seems to appear both as an argument of the function, and as a variable sampled from $D_{t-1}$.\n* How was Appendix Figure 6 (L403) generated?\n* In the theoretical analysis, how would results change if the training set in each step was finite?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* The paper addresses a well-motivated topic.\n* The empirical analysis is grounded in data from real human subjects.\n* Results seem to provide interesting insights into ML-assisted decision-making contexts." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "* This paper examines Human-ML collaboration under performative prediction settings through theoretical analysis and an empirical experiment on ML-assisted combinatorial knapsack problems.\n* In the setup, users interact with a predictive system in discrete time steps. At each time step $t$, a model $M_t$ predicts a label $Y_{M_t}$ based on features $X$. This prediction serves as decision support for a human decision-maker, who then makes their own prediction $Y_{H_t}$​​. Pairs $(X,Y_{H_t})$ are used to train the subsequent model $M_{t+1}$, and it is assumed that $M_{t+1}$​ perfectly aligns with its training distribution. Definition 1 introduces utility $\\mathbb{U}(X, Y)$ for prediction-label pairs, defining its properties axiomatically. It then defines a the collaborative characteristic function which captures one-step utility improvement, and $\\\\mathbb{L}_{\\\\Delta\\\\mathbb{U}}(s,t)$ as the trajectory of expected utilities for a system whose initial utility is $s$. Propositions 1 and 2 show that utility trajectories converge under monotonicity assumptions.\n* The empirical section evaluates the impact of model-based advice on human solutions for the 0-1 knapsack problem. Human participants interact with an ML-supported system to solve knapsack problems, possibly receiving predictions of the optimal solution. Six models with varying accuracy were trained before the experiment using synthetic optimal solutions, and each experimental group received distinct models and possibly different monetary incentives. Results indicate that incentivization schemes had no significant impact on solution quality, while decision-support quality correlated with human solution quality. Collaborative learning trajectories were presented based on these results." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The paper claims to provide an empirical evaluation of performative prediction but seems to lack essential elements of this setup. Specifically, prediction models were trained on synthetic data before the experiment, and the experiment does not include \"feedback loops\" which are a defining component of performative prediction.\n* The theoretical analysis applies to a limited form of performative prediction, assuming that utility trajectories are determined solely by population-wide average utility, without taking the structure of the predictor into account. Functions like the collaborative learning path are interesting, it is not clear whether the definition are applicable in more general scenarios.\n* The empirical approach uses an atypical learning task: predicting a binary solution vector for a combinatorial 0-1 knapsack problem based on random synthetic instances and optimal solutions. The paper notes a possible analogy to multi-task classification, but it’s unclear how results extend to conventional ML tasks on non-synthetic data." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Can the author open-source the dataset provided by real human?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Originality: A substantive assessment of the strengths of the paper, touching on each of the following dimensions: originality, quality, clarity, and significance.\nQuality: The paper is supported by a robust empirical study involving 1,408 participants working on the knapsack problem. The statistical analyses performed provide strong support for the conclusions drawn, particularly regarding human improvements over ML predictions. Additionally, the paper critically examines the impact of monetary incentives on decision quality, contributing valuable insights to the field.\nClarity: The paper's motivation and conclusion are clear.\nSignificance: The paper gives some suggestions about the consideration of human behavior and the selection of the dataset to train the model." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the dynamic model of performative human-ml collaboration from both theoretical and empirical perspectives. The paper introduces the notion, the Collaborative Characteristic Function which connect the predicted label and the unknown ground truth. The paper does some empirical study that involves real human on the knapsack problems. Experimental results show that human tend to improve the model's performance, and human may submit worse results than the prediction by models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.Abstractness of Problem Domains: The study does not focus on specific classification or regression tasks, which makes the findings somewhat abstract. A more concrete application would enhance the practical relevance of the research.\n2.Limited Application Scope: The research primarily concentrates on the knapsack problem, neglecting more realistic scenarios, such as medical diagnosis. Exploring applications in critical areas like healthcare would significantly increase the paper's impact and relevance.\n3.Participant Preference Variability: While the study involves 1,408 participants, it lacks a detailed analysis of their preference differences. Understanding how individual preferences might affect decision-making is essential, as these variations could lead to suboptimal choices in certain instances.\n4.Simulation of Human Behavior: Beyond conducting real experiments with participants, the paper does not explore the potential for simulating human behavior. Employing simulations could reduce the costs associated with extensive human experimentation while still providing valuable insights into collaborative decision-making dynamics." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1.\tThe paper initially presents that current human-ML collaborative systems face three crucial challenges, but the subsequent text does not detail the innovations made in addressing or alleviates these three issues. I hope to see a clear exploration of how the paper addresses or alleviates each of these challenges in the introduction.\n2.\tA deeper discussion on incentive mechanisms: Provide more discussion on the ineffectiveness of incentive mechanisms to help readers understand the potential reasons of this phenomenon.\n3.\tThe contributions are trivial, making readers difficult to understand the key points of this paper. I hope the author can rewrite their contributions.\n4.\tIn Definition 1, the definitions of Ymin and Y' are not specified. In Defination 5, the definition of x1…xn should be placed in the main text." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "This paper has several strengths, as follows:\n1.\tA new dynamic framework is proposed for considering the deployment of ML models in human-ML collaborative systems.\n2.\tThe involvement of participants in real-world scenarios enhances the credibility of the research. The design of the empirical study allows for clear identification of the actual ground truth, providing evidence for the research results.\n3.\tThe findings of the paper have practical significance, aiding companies in optimizing the training and deployment strategies of ML models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper primarily presents a new dynamic framework for thinking about the deployment of ML models in performative human-ML collaborative systems, helping to understand how ML influences human decision-making processes. This research is intriguing and has practical value." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe paper is hard to follow, the complexity may make it difficult for readers to understand.\n2.\tThe research focuses primarily on the knapsack problem scenario, which may limit the generalizability of the results. It is recommended that the authors consider validation in different types of problems to enhance applicability.\n3.\tThe paper mentions the failure to find a positive impact of incentive mechanisms on human decision quality, and the explanation for this phenomenon is insufficient, leading to a superficial discussion of the incentive mechanisms without exploring their potential reasons." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "All my questions are listed in the weakness part." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The studied problem is important. Developing a collaborative system that integrates ML models with human decision-making to consistently achieve better outcomes is both a valuable and challenging topic for academia and industry.\n2. A theoretical framework is proposed to describe the collaboration process and quantify the quality of solutions from both models and humans. A sufficient condition, which ensures non-decreasing utility, is provided to guarantee the achievement of a stable point.\n3. An empirical experiment was conducted with real users, offering interesting insights into practical applications." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a theoretical framework for describing the collaboration process between ML models and human decision-making. By defining a utility function and a collaborative characteristic function, it gives a sufficient condition for achieving a stable point in the optimal case. Additionally, an empirical experiment with real users offers interesting insights into practical applications." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe theoretical framework primarily aims to describe the problem. Both the theory and convergence conditions rely on acquiring the utility function, which seems to be merely achievable with the knowledge of the ground truth. However, as discussed in Introduction and Related Works, a key intuition of this paper is addressing the inaccessibility of ground truth in real-world scenarios. Consequently, the theory offers limited insights at the methodological level.\n2.\tSeveral expressions and derivations are unclear and lack rigor. E.g., it seems that $\\delta_{M_t}$ in Eq. (3) should be determined jointly by ${M_t}$and $X$. In Definition 3 and Proof A.6, $U(H(X, Y_{M_t}))$ should be instead be written as $U(X, H(X, Y_{M_t}))$ instead. Additionally, the logic behind the proof of Proposition 1 is unclear, particularly why $E_{x \\in \\mathcal{X}}(U(Y_{M_{t+1})}=0)$ holds. And Observation 2 is also confusing. Why should the absolute difference in distance measures equate to the difference in utilities?\n3.\tThe authors devote substantial space to describing the experiment and results related to monetary incentives. However, since these are empirical observations of a single confounding factor in a specific scenario, they provide limited insight and generalizability from a broader perspective" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We introduce a new framework for human-ml collaboration that considers distribution shifts and test it in a large user study." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024a,\ntitle={A Dynamic Model of Performative Human-{ML} Collaboration: Theory and Empirical Evidence},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4wmf3Ffhl2},\nnote={under review}\n}" }, "abstract": { "value": "Machine learning (ML) models are increasingly used in various applications, from recommendation systems in e-commerce to diagnosis prediction in healthcare. \nIn this paper, we present a novel dynamic framework for thinking about the deployment of ML models in a performative, human-ML collaborative system. In our framework, the introduction of ML recommendations changes the data-generating process of human decisions, which are only a proxy to the ground truth and which are then used to train future versions of the model. We show that this dynamic process in principle can converge to different stable points, i.e. where the ML model and the Human+ML system have the same performance. Some of these stable points are suboptimal with respect to the actual ground truth. As a proof of concept, we conduct an empirical user study with 1,408 participants. In the study, humans solve instances of the knapsack problem with the help of machine learning predictions of varying performance. This is an ideal setting because we can identify the actual ground truth, and evaluate the performance of human decisions supported by ML recommendations. We find that for many levels of ML performance, humans can improve upon the ML predictions. We also find that the improvement could be even higher if humans rationally followed the ML recommendations. Finally, we test whether monetary incentives can increase the quality of human decisions, but we fail to find any positive effect. Using our empirical data to approximate our collaborative system suggests that the learning process would dynamically reach an equilibrium performance that is around 92% of the maximum knapsack value. Our results have practical implications for the deployment of ML models in contexts where human decisions may deviate from the indisputable ground truth." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Human-AI Collaboration", "Human-Computer Interaction", "Dynamic Systems", "performative prediction", "strategic behavior", "human-in-the-loop", "dynamic learning", "deployment strategies" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/30647409a3fecf97aa80f8333e8d6e3d1213ef69.pdf" }, "presentation": null, "primary_area": { "value": "other topics in machine learning (i.e., none of the above)" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/11b96f2f5ae0305d4b1a52e9bbf37f42efbd1ca3.zip" }, "title": { "value": "A Dynamic Model of Performative Human-ML Collaboration: Theory and Empirical Evidence" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. 263-264: I don’t understand the reasons for setting $V_{w} = V_{c} = V$. Can you elaborate more on why this setting is used? If it is common practice, there should be some citations. \n\n2. It would be helpful to provide a clear definition of parsimony in Section 3.2" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper provides novel insights by studying the mutual information matrix in Skip-Gram Negative-sampling (SGNS) embeddings in hyperbolic space. In particular, demonstrating that distance in hyperbolic embeddings obtained by using SGNS equates to mutual information is an interesting finding that can motivate the use and further study of Poincaré embeddings in NLP. Additionally, the empirical result that hyperbolic embeddings are more unstable during training than their Euclidean counterpart and that more samples are needed to reduce training error can help guide further works in training hyperbolic embeddings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper provides both theoretical and empirical analysis of Skip-Gram Negative-Sampling (SGNS) embeddings in hyperbolic space. While SGNS traditionally embeds words and contexts in Euclidean space (Word2Vec), the authors extend this approach to hyperbolic space using Poincaré embeddings. Two types of errors are used to evaluate the embeddings: spatial error, which is influenced by the dimensions and structure of hyperbolic space, and generalization error, which measures the relationship between embedding error and sample size across different spaces. An empirical study of hyperbolic embeddings is conducted on WordNet and THUNews.\n\nThe authors investigate how hyperbolic distance relates to mutual information, deriving bounds on both spatial and generalization errors. Furthermore, they demonstrate that the distance, d(w,c), between w and c corresponds to the mutual information between w and c in a hyperbolic space. This finding helps to motivate the use of Poincaré embeddings." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Overall, the paper is extremely dense and difficult to follow because it provides little motivation or intuition for mathematical notation.\n\nI understand that one of the paper's main contributions is to provide a detailed mathematical analysis of the mutual information matrix in hyperbolic embeddings. Still, some detail is unnecessary in the main body of the paper and hinders the reader's ability to read the paper. For example, results such as those in section 3.1 that use straightforward algebraic computations to show that distance approximates mutual information should be moved to the appendix. \n\nWhile the paper provides some nice theoretical insights, the methods used for the evaluation of hyperbolic embeddings with Skip-Gram Negative-Sampling are not robust. Using the rank of the restored point-wise mutual information matrix as the sole metric to compare Euclidean hyperbolic embeddings is not particularly interesting. Investigating the performance of hyperbolic embeddings on word similarity tasks, e.g., WordSim-353 or SimLex999, would provide a meaningful quantitative comparison of using embeddings based in different spaces and help motivate the study of static, hyperbolic word embeddings. Further, comparing the performance of classification models that use standard Word2Vec embeddings and hyperbolic Skip-Gram Negative sampling embeddings would provide a much stronger motivation for the paper." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "n/a" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. It directly replaces the distance/similarity measure in learning word2vec, which makes the approach easy to conceptualize.\n2. Under mild assumptions, the submission provides interesting generalization bounds." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "the paper proposed to replace the Euclidean embeddings learned in word2vec with Hyberpoblic embeddings specifically with Poincare geometry. The method is straightforward - rather than using dot-product, a Euclidean-space similarity measure, the submission measures the distance between two word vectors on a Poincare disk. However, the evaluation approach puzzles me." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "It puzzles me that there are many simple 'real-world'-ish datasets for evaluating learned word embedding, but somehow, the submission doesn't provide any of them. IMO, the submission conducts the study as if the problem is orthogonal to NLP.\n\n1. Having an understanding of the sample complexity and how the error bound of the estimation depends on the sample complexity is generally informative, however, in recent years, we have found ourselves in a wacky situation that, for a model to generalize, the training loss just needs to be small, but it doesn't need to be very small, because many plateaus in the loss landscape provide models with good generalization, thus, having a theoretical understanding of the loss function or the error bound becomes somehow outdated.\n\n2. A crucial aspect or consideration of learning on massive corpora is the complexity of the algorithm itself, which the submission doesn't mention.\n\n3. The submission didn't use common datasets for learning word embeddings, nor does it provide any evaluation on common benchmarks, e.g. SimEval or SentEval." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "__Questions__:\n- Why choose 400 Euclidean dimensionality and 2 for Poincare?\n- Table 1,2,3: I don't really understand the reported numbers (what is the distance function exactly in Table 1? What is the distance in Table 2?). I suggest you give an explicit interpretation of those numbers to make it clear to the reader\n- There is a conclusion that training with hyperbolic embeddings takes more time and iterations. However, it's unclear from your experiments if Poincare space embeddings can achieve the same loss as Euclidean ones with a higher number of samples (or iterations) (Table 6 and Table 7) or it is still behind the Euclidean embeddings\n- Lime 418: `Moreover, hyperbolic space requires more than 70,000 samples to achieve adequate training`: what is _adequate_? How do you define it? \n\n__Writing__:\n- Table 7 has the incorrect title. It's 400-dimensional Euclidean space, not Poincare space" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- I strongly believe exploring the embedding spaces beyond Euclidean space is crucial for the field\n- Theoretical and empirical results are provided\n- Reflection on the advantages (low-dimensionality) and disadvantages (training instability, large sample size etc.)" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Hyperbolic embeddings were introduced in the literature as an alternative to the embeddings in Euclidean space. This paper provides an analysis of the skip-gram embedding model in hyperbolic space. The authors offer their take on many dimensions of the hyperbolic embeddings, including their connection to the mutual information matrix, generalization capabilities (with theoretical proof), and required sample size/training stability. Theoretical results are further supported by empirical results on two datasets: Wordnet and THUNews." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Although it's crucial and interesting to explore various properties of hyperbolic embeddings, they do not exist in a vacuum, so it would be useful to see the performance of the embeddings on downstream tasks\n- Provided experimental setup and results are hard to follow (see questions)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "See my questions in Weaknesses." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1) Connecting hyperbolic embedding with mutual information is interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper discusses the relationship between the point-wise mutual information matrix and the hyperbolic distance. Furthermore, the authors establish generalization error bounds for hyperbolic embedding. These bounds demonstrate the dimensional parsimony of hyperbolic space and its relationship between the generalization error and the sample size. Experiments on the Wordnet dataset and the THUNews dataset validate the theoretical properties." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) The motivation of connecting hyperbolic embeddings and PMI is unclear. Both are distance measure, hyperbolic distance captures similarity of hierachies, while PMI quantifies the discrepancy between the probability. What do you mean by the “equivalence between the Gramian matrix in hyperbolic embedding and the dimension of the space”? \n2) What is the research questions that you want to answer in the Experiment section? The authors said that the theoretical findings are evaluated by conducted the experiments. However, it is unclear how the experimental results related to the theoretical findings. Which theorems (theorem 1 or 2? ) you want to answer? It would be much clear if the authors list the research questions. What do you really want to evaluete and compare. \n3) I could not understand what do the tables in the experiment section want to tell us? perhaps the authors want to show some correlation between dimension and mutual information matrix? then it is better to plot it with some line plots." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We analyze the relation between hyperbolic embedding and mutual information, and give a generalization error bounds for hyperbolic embedding." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024the,\ntitle={The Mutual Information Matrix in Hyperbolic Embedding and a Generalization Error Bound},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4wpqmhh05N},\nnote={under review}\n}" }, "abstract": { "value": "Representation learning is a crucial task of deep learning, which aims to project texts and other symbolic inputs into mathematical embedding. Traditional representation learning encodes symbolic data into an Euclidean space. However, the high dimensionality of the Euclidean space used for embedding words presents considerable computational and storage challenges. Hyperbolic space has emerged as a promising alternative for word embedding, which demonstrates strong representation and generalization capacities, particularly for latent hierarchies of language data. In this paper, we analyze the Skip-Gram Negative-sampling representation learning method in hyperbolic spaces, and explore the potential relationship between the mutual information and hyperbolic embedding. Furthermore, we establish generalization error bounds for hyperbolic embedding. These bounds demonstrate the dimensional parsimony of hyperbolic space and its relationship between the generalization error and the sample size. Finally, we conduct two experiments on the Wordnet dataset and the THUNews dataset, whose results further validate our theoretical properties." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Hyperbolic embedding", "Mutual information", "Generalization error bounds" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/9c9f6370badd68f1aad3b3bd5314a4a60d120fe6.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "The Mutual Information Matrix in Hyperbolic Embedding and a Generalization Error Bound" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. The S7 should combine the advantages of the S5 and S6, so it needs to be compared with both. E.g., why is it simpler than the S6? Training faster? Fewer parameters?\n2. Why did the introduction of selective matrix degrade the effectiveness so much on long sequence tasks like path-X in LRA, compared to S5 (Line 402)? More analysis is needed, otherwise the S7's improved sequential modeling capabilities using input-dependent matrix don't seem useful and convincing.\n3. Prior work showed that the performance of deep state space models are sensitive to the initialization of the state matrix. [1] Have you done experiments with different initializations to verify the robustness of the S7 module? Along as the experiments of effectiveness of Stable Reparameterization for Long-Term Dependencies.\n\n[1] Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré. Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in Neural Information Processing Systems, 34, 2021b." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. It turns out that the combination of S5 and S6 is helpful for SSM structure and contributes to the development of SSMs.\n2. Experiments are conducted on many datasets together with extensive analysis." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed a new SSM called S7 for sequence modeling. It combines the strengths of S5 (simpler structure) and S7 (input-dependent state transitions) and incorporates stable reparameterization of the state matrix. Many experiments were carried out to verify the efficiency and performance on different datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The core contribution of this paper seems to be successfully combining S5 and S6, with many experiments being carried out. However, the paper lacks a detailed introduction to the S5 and S6, so readers may not be completely clear about the advantages and disadvantages of these two models, and how S7 surpasses the two. E.g., why to say \"S6 introduces hardware-specific complexity\" (Line 88)? Some details of S5 and S6 can be shown clearly in 3.1 Background.\n2. An intuitive comparison of the S4 (S4D or DSS), S5, S6, and S7 schematic can be given, to clearly show the development and difference of SSMs. Or a similar part like 4. RELATIONSHIP BETWEEN S4 AND S5 in S5 paper [1].\n3. The effectiveness of Stable Reparameterization of S7 seems not to be verified. More introduction to the initialization of the state matrix should be given, or the writing logic of Stable Reparameterization for Long-Term Dependencies (Line 211) is too hard for readers to follow.\n\n[1] Smith, J. T., Warrington, A., & Linderman, S. W. (2022). Simplified State Space Layers for Sequence Modeling. ArXiv. https://arxiv.org/abs/2208.04933" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1.\tWhat does S7 mean? (is the ”7” with some particular meaning?)\nOther questions please see the above weaknesses." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper proposes an input-independent mechanism for SSM, with stable reparameterization techniques, and it provides the stability of this reparameterization through comprehensive theoretical derivations." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes an SSM (state space model) architecture call S7, to provide an input-dependent mechanism for an existing work (S5), and showing this architecture can efficiently and accurately deal with sequence modeling tasks. The experiments show it performs better than Mamba(S6) in LRA tasks, while worse than other SSM-like models, and show it achieves good performance in neuromorphic datasets and dynamic prediction tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe novelty is not very clear to me, as mentioned in the paper, it is claimed that this paper provides efficient input-dependent state transition dynamics based on S5, but conceptually, it is the same as what Mamba (S6) did for S4, that introduced learnable matrices A, B, C\n2.\tThe paper claims this S7 is more efficient than Mamba, but I did not find any experiments data on the efficiency comparison with Mamba/Mamba2. Theoretically, without using parallel technologies like selective scan or others, how could one run the S7 in an efficient way when at each time step one needs to update the dynamics of A, B, C, D? \n3.\tThe experiments do not well support the claims: the performance of S7 in LRA is substantially worse than many other methods, and there’s no results showing it’s better than mamba in general language modeling tasks (which is an important selling point of mamba-like models). This leads to a question: what is the use case of S7?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "1. **Could the authors clarify the novelty of the reparameterization?** How does it differ from StableSSM’s reparameterization framework?\n\n2. **Why was the S5 model chosen as the basis for S7?** Given that S6 with the StableSSM reparameterization might provide similar benefits, what informed this design choice?\n\n3. **Could the authors specify if the neuromorphic-specific design applies solely to neuromorphic tasks or to all benchmarks?** This would improve clarity regarding the model's consistency across different tasks.\n\n4. **What is the author’s perspective on improving the model's performance on data whre time-dependance is note relevant?** Given S7’s limited success on LRA, is there a feasible modification that could address these challenges while preserving input-dependence?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "*Input-Dependent Dynamics:* S7’s adaptation of the S5 model to be input-dependent is a promising approach. This could enhance the model’s responsiveness to input variability, a significant issue in long-range sequence tasks.\n\n*Stable Reparameterization:* The model claims to maintain gradient stability over long sequences, addressing gradient explosion/vanishing issues commonly faced in deep learning. This feature has potential benefits for training efficiency and stability.\n\n*Broad Applicability:* S7’s successful application across various domains, from physical time series to neuromorphic vision, suggests it may generalize well to different task types." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents S7, a simplified state-space model (SSM) designed for long-range sequence tasks. Building on the S5 model, it introduces input-dependence to allow dynamic adjustments to state transitions based on input content. The paper claims S7 achieves stable reparameterization and efficient performance across diverse tasks, including neuromorphic event-based datasets and Long Range Arena (LRA) benchmarks, without requiring the complexity of models like S4 or S6 (Mamba). The proposed model is argued to balance computational simplicity with adaptive filtering and content-based reasoning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**Limited Novelty:** The paper is introducing an input-dependent update mechanism (already introduced by S6) stabilized through the reparameterization framework and key equations (Eqs. 6, 7, and 8) borrowed directly from StableSSM [1], raising concerns about the originality of the contributions. The paper only states that it was “inspired” by stable reparameterization, yet much of the core methodology relies on prior work.\n\n\n**Inconsistent Notation:** The notation for $𝐴_k$ is unclear, with dependency on input appearing inconsistently (e.g., it appears in Eq. 5 and line 267 -- e.g. $𝐴_k(u_k, \\theta_m)$ but is omitted elsewhere -- e.g. $𝐴_k(\\theta_m)$. This lack of uniformity in notation undermines the model’s theoretical presentation.\n\n**Weak Justification for S5 Model Selection:** S5 is mentioned as the basis for S7, but no rationale is provided for not using S6 (Mamba) and the reparameterization technique from StableSSM. Moreover, no connection or description of the S5 model is given (MIMO approach etc.)\n\n**Assumptions Clarity:** Assumptions (3.1, 3.2, and 3.3) are not well justified or examined for feasibility, and the text lacks guidance on implementing or verifying these assumptions. This leaves important theoretical aspects of the model unaddressed.\n\n**Unclear Contribution of Neuromorphic Design:** The neuromorphic-specific design choices in Section 3.4 seem disconnected from the rest of the model’s development (no other mention on the first part of the paper). It’s unclear whether these additions (Eq. 11, 12) apply exclusively to neuromorphic tasks or extend to other benchmark tasks.\n\n**Lack of Benchmark Justification:** The paper does not clarify why specific datasets were chosen. For instance, given the input-dependent nature of S7, benchmarks used by similar models like Mamba (e.g., Selective Copy or Induction Heads or other similar benchmarks -- see Section 7/Table 4 of the thematic survey [2]) might have been more appropriate for comparison.\n\n**Poor Performance on LRA Benchmarks:** S7’s subpar performance on LRA benchmarks raises concerns about its applicability to heterogeneous data. The authors provide only a brief discussion, without substantial insight or proposed solutions for improving performance on these challenging tasks.\n\n\n[1] Wang, Shida, and Qianxiao Li. \"Stablessm: Alleviating the curse of memory in state-space models through stable reparameterization.\" arXiv preprint arXiv:2311.14495 (2023).\n\n[2] Tiezzi, Matteo, et al. \"State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era.\" arXiv preprint arXiv:2406.09062 (2024)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Is the code to reproduce the results publicly available?\n- How is lambda_k computed?\n- Fig.1 delta_k is not defined in the caption\n- Line 104-109 this sentence is too long and could be split in 2-3 smaller ones.\n- What is the performance of other SSMs in Table 5, 6 and 7?\n- In which regards in the S7 simpler or superior over other SSMs? Number of flops? Number of parameters?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The authors evaluate their model on a series of benchmark tasks\n- The authors provide some theoretical analysis for the training stability" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a novel state-space model (SSM) called S7 that introduces input-dependent dynamics to filter and retain relevant input information over extended time horizons. An important part of S7 is a reparametrization trick that ensures training stability. S7 is claimed to reach a balance between performance and model's complexity for processing very long sequences." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Scientific novelty of the manuscript: the authors base their work on the existing S5 model and extend it with the input-gating, that other models, such as Mamba already have demonstrated. In fact, it remains even unclear when reading through the main paper how the input-gating is realized precisely. The reader misses those equations. I.e., How is Lambda_k computed?\n- The selection of the tasks and in particular the selection of the reference models is a major weakness of the model:\n 1. since the S7 model is based on the S5 model, it is of paramount importance that one always compares to S5 at least, which the authors do not do for many datasets they considered. For example, this comparison is missing in Table 5, 6 and 7 (wrongly called Figure 2 in the manuscript).\n 2. looking at the LRA results, one can see that S7 is only in 2 tasks slightly better than S5, but in the remaining tasks it is significantly worse. Moreover, in Table 5, 6 and 7 (wrongly called Figure 2 in the manuscript), the authors don’t even compare to the S5.\nthe authors claim to introduce a simpler model than Mambda, but it remains unclear in what regards it is simpler, e.g., if it uses less number of parameters, or less computations, this needs to be demonstrated in the results.\n- Some minor comments are: The authors are very much overstating their novel contributions with terms such as “S7 has demonstrated its superiority”, which is by no means true. Figure 2 on page 10 should probably be Table 7" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "A new sequence model for long sequence modeling employed on various tasks." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024s,\ntitle={S7: Selective and Simplified State Space Layers for Sequence Modeling},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4wtcXV0kbi},\nnote={under review}\n}" }, "abstract": { "value": "A central challenge in sequence modeling is efficiently handling tasks with extended contexts. While recent state-space models (SSMs) have made significant progress in this area, they often lack input-dependent filtering or require substantial increases in model complexity to handle input variability. We address this gap by introducing S7, a simplified yet powerful SSM that can handle input dependence while incorporating stable reparameterization and specific design choices to dynamically adjust state transitions based on input content, maintaining efficiency and performance. We prove that this reparameterization ensures stability in long-sequence modeling by keeping state transitions well-behaved over time. Additionally, it controls the gradient norm, enabling efficient training and preventing issues like exploding or vanishing gradients. S7 significantly outperforms baselines across various sequence modeling tasks, including neuromorphic event-based datasets, Long Range Arena benchmarks, and various physical and biological time series. Overall, S7 offers a more straightforward approach to sequence modeling without relying on complex, domain-specific inductive biases, achieving significant improvements across key benchmarks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "state space models", "neural network architectures", "deep learning architectures", "sequence modeling", "event-based vision", "event cameras", "neural odes" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/1fffab6aa0f02c9b5326306d49f2faa5435961a9.pdf" }, "presentation": null, "primary_area": { "value": "learning on time series and dynamical systems" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "S7: Selective and Simplified State Space Layers for Sequence Modeling" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see the above." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* Simplicity and Effectiveness: One of the major strengths of this paper lies in its approach’s simplicity. Layer shuffling and task vector superposition are straightforward yet powerful techniques that effectively reduce interference without needing additional training, optimization, or complex configurations. This simplicity not only enhances the practicality of the approach but also makes it easy to implement and adapt across various multi-model compression tasks, proving that even minimal adjustments can yield significant performance improvements.\n\n* Effective Interference Reduction: The combination of layer shuffling and task vector superposition is innovative in addressing interference by increasing orthogonality among task vectors. This approach allows for a more effective merging process, yielding improved model accuracy without the need for additional optimization or training steps.\n\n* Adaptability and Scalability: The proposed method’s flexibility is a clear strength. Its data and model-independent nature enables seamless additions and removals of models (hot-swapping) without re-computation, a valuable feature for dynamic applications. Moreover, the approach is efficient, doubling the memory footprint while providing significant accuracy improvements.\n\n* Comprehensive Evaluation: The experiments cover a range of benchmarks and tasks, showcasing the model’s capability across various domains, from image classification to text generation. This breadth of evaluation helps establish the generalizability of the method across tasks and model architectures." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces two methods, layer shuffling and task vector superposition, aimed at reducing interference between task vectors in multi-model compression scenarios. The proposed methods work by increasing the orthogonality of task vectors, thus minimizing their interference during merging. By leveraging randomization, these methods require no additional training and can be applied across various models and tasks. Experiments on multiple benchmarks, including CLIP, Flan-T5, and GPT-2, demonstrate that this approach can achieve comparable performance to fine-tuned models while reducing storage costs, particularly for real-world deployment scenarios where adding or removing models on the fly is necessary." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* Lack of Detailed Performance Analysis Based on Shuffle/Superposition Levels: It would be useful to analyze the impact of different levels of shuffling and superposition, as these levels could influence task vector similarity and interference differently. This analysis would provide a clearer picture of optimal interference reduction strategies.\n* Clarity Issues in Method Description: Some aspects of the method, such as the merged task vector formation in equation (8), could benefit from further clarification. Specifically, does shuffling task vectors in different layers cause mixing of task vectors across layers, for instance, between k-1 or k+1? Clarifying this would enhance understanding of how the shuffle affects layer-specific task vector alignment.\n* Effectiveness Across Tasks: The effectiveness of either TA+Shuffle or STA appears to vary by task, yet the paper does not discuss why some tasks benefit more from specific strategies. A more in-depth analysis here would provide insights into optimizing methods based on task characteristics.\n* Related Work Reference (PEFT): (Maybe, long shot) this paper is related? \"Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals,\"\n* Minor Formatting Issues: There are some minor formatting errors in the document, such as incorrect Latex punctuation and inconsistent reference formatting. For example, equation 3 is mistakenly referenced in the context of equation 4, and parentheses are missing in certain citations. Additionally, clarifying what the values in parentheses mean in tables, such as in the average (%) and Bits (Gb) columns, would be helpful, as it currently requires reading the text to understand that they refer to relative performance to fine-tuned models." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. why is 5.03 the number of Gb attributed to fine-tuned? shouldn’t it be 8x the pre-trained model?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The method is intuitive and simple. The motivation for both components is well written and properly ablated.\n2. The method is scalable and memory efficient (modulo the duplication of model parameters) given that it only requires the storage of random seeds to retrieve the final model.\n3. The experimental results are strong across benchmarks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes two stochastic mechanisms to improve performance for multi-task model merging by reducing task interference. First, the method takes advantage of the repeating structure of modern neural networks and randomly shuffles the same-module layer across blocks by first showing that the layers are mostly similar in the within-block across tasks. Second, the paper proposes random binary matrices to multiply parameter vectors to further reduce the task vector similarity. During inference, the inverse transforms are applied. The paper performs experiments across diverse benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The method has a limitation that is not discussed a lot, apart from the title: it requires the knowledge of the task id during inference. This needs to be underlined during the comparison with methods such as ties and task arithmetic for fairness.\n2. lack of forward pass time complexity comparison. The proposed method introduces an overhead in the forward pass: layers need to be reshuffled in the correct order, signs need to be restored and the residual needs to be added to the pre-trained weights. Therefore, there should be a study of how much overhead all these operations incur.\n3. Missing baselines: Given the parameter increase and the time complexity overhead, the paper should compare with the compression algorithm of [1].\n4. The paper solely focuses on small models, base variants on ViT and Flan-T5, but the literature uses ViT-L/14 and T5-XXL regularly. It would also be interesting to check the performance of the method as tasks increase, see 14 and 20 task benchmarks from [1]. It would be interesting to also track the forward pass time metrics in the case of larger models.\n5. L268-269: fix references for benchmarks: the vision one for instance comes from Ilharco et al. and not from FusionBench\n6. Baselines and their categorization are not explained and the reader cannot understand why PSP ins included given its poor results or what WEMoE and SMILE are on their own category compared to everything else. It would be helpful for the reader to provide a brief description of each method as well as a high level overview of the categories to help the reader understand rather than deferring to the appendix where they are actually not discussed.\n7. Extremely limited Related work: the quality of the paper is heavily undermined by the lack of proper references and discussion over related work.\n\nMinor Comments\n\n\n- L202: ontain → obtain\n- Rephrase informal writing:\n\t- L217: “which we expect to be much lower”\n\t- L150: “but this balance has generally been tricky to achieve”\n\n[1] Wang, K., Dimitriadis, N., Ortiz-Jimenez, G., Fleuret, F. and Frossard, P., 2024. Localizing Task Information for Improved Model Merging and Compression. *arXiv preprint arXiv:2405.07813*." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Q1: Although randomization offers clear advantages like data independence, would a more systematic approach to orthogonalizing task vectors further improve the performance?\nQ2: Did you observe any (in-)consistent performance variance due to randomness in shuffling and superposition?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper makes an observation that individual task vectors are too similar and successfully uses it to reduce task vector interference, leading to better multitask performance in model compression scenarios.\n- Both proposed techniques operate without needing data, allowing flexible model addition or removal without retraining or optimization.\n- The method achieves storage reduction compared to keeping individual models.\n- The approach is shown to improve performance across diverse domains including image classification, text generation, and text classification.\n- The method enables on-the-fly model integration, allowing seamless \"hot-swapping\" of models.\n- The paper is very well written and clearly structured." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces two methods, Layer Shuffling and Task Vector Superposition, aimed at reducing interference when compressing multiple fine-tuned models into a single multitask model using task vector arithmetic. Layer Shuffling works by randomly reordering layers in each model before merging, reducing alignment between task vectors. Task Vector Superposition applies random orthogonal transformations to further decorrelate task vectors. Both techniques minimize interference and improve performance across tasks. Experiments with CLIP-ViT, Flan-T5, and GPT-2 show that this approach achieves higher accuracy than vanilla task arithmetic and other baseline methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While the paper compares its method to several baseline techniques, it misses comparison with closely related recent works, particularly Guillermo Ortiz-Jimenez et al.'s work (mentioned in the paper) on task vector manipulation for model merging. Including these comparisons would strengthen the submission.\n- Although the authors claim minimal memory overhead, additional context matrices and shuffled task vectors nearly double the memory requirement, which may not always justify the marginal performance gains over baselines like SMILE.\n- LoRA results show that SMILE achieves a better tradeoff between accuracy and memory than the reported combination of the proposed methods." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "see the above weakness part" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper presents a novel approach to multi-model compression through random mechanisms\n\n- The empirical evaluation is conducted across multiple benchmarks to demonstrate the effectiveness of the method" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces Layer Shuffling and Task Vector Superposition, two random mechanisms to reduce interference in multi-model compression by increasing orthogonality between task vectors. The methods achieve near-identical accuracy to individual fine-tuned models while reducing storage costs by 4 times and enable seamless hot-swapping of models without recomputation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The current presentation and writing require significant improvements. For instance, the mathematical analysis is overly simplistic and does not warrant extensive explanation. Additionally, the proposed method lacks a rigorous proof demonstrating why Layer Shuffling specifically enhances orthogonality more effectively than other potential random transformations.\n\n- The interaction between Layer Shuffling and Task Vector Superposition isn't thoroughly analyzed as it's unclear whether they're truly complementary or if one method dominates the benefits\n\n- The experiments are not convincing because the models used for comparison are generally much smaller, leading to expected inferior performance from competitors. Meanwhile, the authors' model is significantly larger, resulting in better performance, which does not necessarily demonstrate an advantage." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We present two complementary random mechanisms to significantly reduce interference when eliminating cross-model redundancy for efficient multi-model serving: Layer Shuffling and Task Vector Superposition." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024interfering,\ntitle={Interfering with Interference: Blind Shuffling and Superposition for Better Multi-Model Compression},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4wuvmJRAU4},\nnote={under review}\n}" }, "abstract": { "value": "We present two complementary random mechanisms to significantly reduce interference when eliminating cross-model redundancy for efficient multi-model serving: _Layer Shuffling_ and _Task Vector Superposition_. They work together to increase the orthogonality among interfering task vectors, forcing them into self-destruction without requiring any post-training learning or optimization. _Layer Shuffling_ randomly reorders layers of each individual models to reduce the alignment between interfering task vectors. While _Task Vector Superposition_ leverages random orthogonal transformations to decorrelate task vectors further. Together, these techniques drastically minimize interference, yielding improved performance across multiple tasks with effectively zero incremental memory cost when incorporating new models. Their data and model-independent nature also allows for seamless on-the-fly addition or removal of models, without requiring any re-computation, making them highly practical for real-world deployment scenarios." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Task Arithmetic", "Superposition", "Model Merging", "Multi-model Compression", "Model Serving" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/a03ddaa98be0df5602b6392b114f90e81d3b40d8.pdf" }, "presentation": null, "primary_area": { "value": "transfer learning, meta learning, and lifelong learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Interfering with Interference: Blind Shuffling and Superposition for Better Multi-Model Compression" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See Weaknesses.\n\nTypos:\n1. line 19: propeties -> properties\n2. line 86: exihibit -> exhibit\n3. line 527: thire -> their" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper tested a very meaningful assumption of whether simple language in training data can lead to better generation abilities of SLMs.\n2. The readability measurement approaches are comprehensively studied and analyzed." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors investigates the impact of training data's readability to the generation abilities of very small language models (SLM). They challenge the claim that training SLMs on simple language is the reason for their ability to generate coherent text. They create synthetic corpora with varying level of readability, and found no impact to the coherence of text generated by SLMs, and also found training on simple language does not lead to earlier development of coherence during training." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The quality measurement is limited to perplexity and coherence (coherent, according to the llm-as-judge prompt, is considered \"well-structured and well-organized\", \"not just a heap of related information, but should build from sentence to sentence\"). The ignorance of other dimensions of quality (for example, as authors also mentioned, clarity and fluency) makes any statements about \"generation abilities of SLMs\" an overclaim.\n2. The quality measurement doesn't use any metrics from the original TinyStories paper: grammar, creativity, consistency with the beginning of the story (Eldan & Li, 2023). That makes the results from the two papers in comparable. Because of that, there is no evidence that \"SLMs trained on data with substantially more complex language also exhibit the same abilities as those trained on simple language\" can also hold the measurement in Eldan & Li (2023).\n3. While the authors rule out some factors not contributing to coherent SLMs, it is unclear what factors are contributing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I would love to hear the authors' response to my interpretable of the tables / figures, in case there is any misunderstanding.\n\nI am open to raising my score if there is a strong argument for why correcting this misunderstanding is important for the community, as it is my main concern about the paper." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "- The paper is cleanly scoped and clearly written.\n- It corrects a widespread misinterpretation of a result in the NLP literature. This result has been used to motivate LM development inspired by human language learning." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the question: are small LMs capable of learning TinyStories because it is *readable* (i.e., simple vocabulary, concepts, and grammatical structures) or some other feature, notably the dataset's lack of diversity (templated sentences with uniform structure)? The authors of TinyStories, and subsequent citations, only consider the former interpretation, but there is no evidence to eliminate the latter.\n\nThis paper carefully investigates this question by generating two datasets with the same synthetic data generation process, differing only in the vocabulary and the intended audience that the model is asked to use & consider. They call these two datasets $\\\\texttt{LlamaTales-Jr}$ and $\\\\texttt{LlamaTales-GRE}$. The two datasets are equally coherent, but $\\\\texttt{LlamaTales-Jr}$ is much more readable. They find that small LMs are *equally* capable of learning both $\\\\texttt{LlamaTales-Jr}$ and $\\\\texttt{LlamaTales-GRE}$, showing that *readability* does not necessarily explain small LMs' ability to learn TinyStories. Instead, they hypothesize it is the lack of diversity in the data." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The scope of the paper is relatively narrow. While it shows that the community has widely misinterpreted the results of a particular paper, it's not clear how much it matters. Moreover, I believe the main surprising finding of $\\\\texttt{TinyStories}$ still stands, which is that SLMs are capable of learning the language of 3-4 year olds (regardless of why).\n- I believe the overall paper can use some reorganization.\n - I find it odd that §3 and §4 (which are all about measuring the readability and quality of the existing dataset, $\\\\texttt{TinyStories}$) are ordered before §5 (about constructing the datasets used in this paper). Wouldn't it make more sense to first describe the data creation methodology, *then* validate that they have the expected readability and quality? Right not, we don't get to the meat of the paper until halfway through page 7.\n - The connection between figures and claims in the running text of the paper is all over the place. For instance, most of the main claims in §3 and §4 are supported by figures in the Appendix.\n- The presentation of tables and figures can be more readable.\n - Figures 2, 3, 6 are hard to interpret due to lack of textual explanation, and I think there must be a better way to present the results. My understanding is that in Figure 2, I should see that in (b), the *green* dots (SLMs trained on $\\\\texttt{LlamaTales-Jr}$) are approximately as high as the best gray dots (LLMs), and in (c), the *blue* dots (SLMs trained on $\\\\texttt{LlamaTales-GRE}$) are ALSO approximately as high as the best gray dots (LLMs). Wouldn't it be better for these to be on the same axes, so the reader can compare directly whether LlamaTales-GRE is as learnable as LlamaTales-Jr? Subplots (a) for $\\\\texttt{TinyStories}$ and (d) for $\\\\texttt{FineWeb}$ should be in the Appendix, since they aren't used to support the main claims. I'm not sure what Figure 3 is doing in the main paper, since it's not discussed in the running text.\n - Table 1 contains results for many metrics which are not discussed in the running text of the main paper. To prevent reader confusion, I recommend moving the results for these metrics to the Appendix, where the metrics are described. The different metrics also don't seem to tell a different story.\n - I recommend a table with examples from $\\\\texttt{LlamaTales-Jr}$ and $\\\\texttt{LlamaTales-GRE}$." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "When measuring quality, you use a set of open models. Why not simply use a state of the art model such as GPT-4o or Claude3.5 instead?" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "The paper provides a new dataset with some added features in relation to a previous dataset (TinyStories)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper discusses the effects on training small language models by changing some features related to the concept of readability of one particular dataset. The experiments show no effects." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The scientific contribution of this paper is limited, as it tackles a very narrow (and somewhat artificial) research question, and the experiments show no discernible effects whatsoever. The research question is somewhat artificial in the sense that the concept of readability (in humans) concerns the cognitive load of *interpreting* a text, which is not the same thing as *learning* a statistical language model from a text. In particular since readability is usually defined in terms of features related to frequency and length of individual tokens, but the paper does not discuss the influence of tokenization on the learning abilities of language models. It is therefore not at all clear (to me) why the concept of readability would have anything at all to do with how well a statistical language model performs. The experiments included in the paper confirms that it does not. The paper also contains an experiment that shows that the concept of readability and the concept of text quality (as interpreted in terms of perplexity and coherence) are unrelated, which is exactly what you would expect given the definition of these concepts. As such, it is difficult to see what novel knowledge this paper contributes with." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024studying,\ntitle={Studying the Effects of Training Data on Small Language Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4xBew7kuYB},\nnote={under review}\n}" }, "abstract": { "value": "Prior work has found that training very small language models (SLMs) on synthetic children's stories allows them to generate coherent text, comparable to much larger models. These stories are claimed to encompass the vocabulary and factual knowledge base of a 3-4 year old child, capturing the \"essence of natural language.\"\nBecause of these claims, it is tempting to attribute the findings to the simple language of children's stories, drawing a parallel to how children learn language.\nIs the human concept of readability relevant in the context of language model training, or are these findings better explained by other propeties of the data?\nIn this study, we investigate this by first validating several automatic readability measures. We then create synthetic corpora with varying levels of readability and assess the coherence of text generated by SLMs trained on these corpora.\nWe find no relationship between the readability of training data and the generation abilities of SLMs. Specifically, SLMs trained on data with substantially more complex language also exihibit the same abilities as those trained on simple language. Moreover, training on simple language does not lead to earlier development of coherence during training." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "small language models", "pretraining" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/407060a3df878a3a13c95e416ede391a6d5cd987.pdf" }, "presentation": null, "primary_area": { "value": "other topics in machine learning (i.e., none of the above)" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Studying the Effects of Training Data on Small Language Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Have you tried running experiments without angular constraints but only radius constraints (to test against the lower variance of the prior effect)?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1.\tThe introduction of hyperspherical coordinates to better capture samples obtain from high dimensional Gaussians is useful especially in the context of constraining the latent variables\n2.\tThe paper attempts to establish an ambitious connection between replica symmetry breaking in spin glasses and their proposed modifications of the loss function that avoids ‘high sparsity’ equatorial regions of the hypersphere." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose to convert the latent variables of a VAE to hyperspherical coordinates. This allows them to constrain samples from the prior distribution to a small region in latent space especially if the latent dimension is large. They provide experimental evidence that this improves the performance of the VAE when generating new data. They also provide some theoretical justification arguing that the sparsity of the latent space impairs the smoothness of the latent manifold." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tThe connection to spin glasses is made via a formal similarity of the energy function of a spin glass and their regularization term (equation 8). This connection appears weak as it does not explain: a) how the regularization helps escaping local minima that correspond to low-quality outputs of the VAE, b) the effect on Parisi’s order parameter which seems at the heart of the spin glass theory of neural networks, and c) the role of temperature i.e., the learning rate in the proposed scheme by which desired low-entropic states are reached.\n2.\tI am not sure that sparsity of the latent representation is the root cause of poor generative performance in VAEs. “Posterior collapse” seems a more likely explanation (also of the empirically observed improvements in section 4) as the proposed constraints not only compress the volume but also simply decrease the variance of the prior distribution. \n3.\tAs a minor point, in line 189-190 the authors see sparsity as an impediment to learning a data representation as a smooth manifold. I am not sure I agree unless the objective would be to explicitly construct the manifold (e.g., via simplicial complexes). But the manifold hypothesis (like the spin glass model) is only a conceptual aid (of how to think about VAE representations of data) not a fully developed theory from which algorithms and their convergence properties can be derived." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Could you provide comparisons with hyperspherical VAEs or other structured latent space models?\n2. Have you explored interpolation in the latent space to support your density claims?\n3. Why were only two datasets used? Including more diverse datasets could better validate your method.\n4. Why would a latent space as tightly concentrated as shown in Figure 1 be desirable? This remains unclear." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "• Innovation and Relevance: The use of hyperspherical coordinates in latent space with principles drawn from statistical physics is a unique and potentially impactful innovation.\n• Clear Problem Statement: The paper presents the problem of sparse latent spaces in VAEs clearly, providing a well-motivated solution.\n• Practical Usability: The proposed method integrates easily with current VAE models and introduces a manageable computational overhead.\n• Clarity of Presentation: The visual support, especially in Figure 1b, effectively illustrates the approach, aiding in understanding the latent space adjustments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents an enhancement to Variational Autoencoders (VAEs) by introducing a hyperspherical latent space with a novel loss function. The method aims to improve generative quality by concentrating embeddings in denser latent regions, moving them away from the equatorial band often associated with sparsity. The approach offers compatibility with existing VAE structures, requiring minimal adaptation to the standard VAE framework." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "• Limited Experiments: The method is only evaluated on two datasets (MNIST and another dataset). More examples of generated samples and interpolations would better support the claim of improved density in latent space.\n• No Comparison with Relevant Competitors: The paper lacks comparative experiments with established hyperspherical VAEs, such as Davidson et al. (2018), or with Riemannian approaches like “A geometric perspective on VAEs” by Chadebec and Allassonniere.\n• Inconclusive Results: The results in downstream tasks like classification are mixed, and the paper’s claims of denser latent space do not\nconsistently reflect in performance metrics.\n\nWhile the paper offers valuable theoretical insights, additional empirical support is needed to substantiate the generative benefits relative to state-of-the-art sampling methods." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See the weakness section above." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper is well written. \n- The relevant works are clearly listed. \n- The idea of drawing insights from high dimensional physical systems is interesting." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a new way to formulate the latents of VAE on hyperspherical coordinates. They use real MNIST data to show the improved generalization ability of VAE." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The experiment results section is weak.\n- The evaluation is only qualitative comparison. Is there any quantitive metric (e.g. classification/prediction error) that can be used to compare the proposed method with existing methods?\n- Lack of comparison with other related hyperspherical VAE work listed in the section 2. related works. e.g. Hyperspherical Variational Auto-Encoder’ Davidson et al. (2018), Yang et al. (2023), Bonet et al. (2022), etc. \n- The paper only shows results on one real dataset. How well does the proposed method generalize to more datasets?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- What are the main limitations of this method?\n- Could the authors provide a comparison with additional baselines and training times? It would be valuable to compare the generative performance and training times of the proposed method with other state-of-the-art approaches to evaluate whether the additional computations required for using hyperspherical coordinates are justified by the improvements in generation performance.\n- The authors state that ‘the random samples of an independent multivariate Gaussian distribution fall in the equator of a hypersphere, and thus none of them is near the singularities of the hyperspherical coordinates’. However, once the latent samples are forced away from the equator, could it be possible to fall near the singularities of the hyperspherical coordinates?\n- The authors have presented generated images only on MNIST, a relatively simple dataset that does not require a high-dimensional latent space to capture its features. As a result, introducing additional constraints in the latent space does not appear to limit its capacity to represent information. However, with more complex datasets, how can these constraints affect the expressivity of the model given that the representations tend to overlap (as stated in Figure 1)? \n- Comparing the results in Figure 2 is challenging because the configurations (dimensionality of the latent space) are positioned at different points (not aligned). Consequently, it is difficult to determine in which configurations or regimes the VAE with hyperspherical coordinates surpasses the vanilla VAE and vice versa.\n- In section 4.4, the authors generate new data sampling from a von Mises–Fisher distribution with the same mean and covariance as the ones empirically calculated from the latent embedding of the full test dataset. What is the motivation to use the empirical statistics of the test set instead of the training set for generation? \n- Could this method be extended/generalized to other distributions?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- To the best of my knowledge, this work introduces a novel approach to improve generative performance in VAEs by constraining latent representations, exploiting the hyperspherical coordinates formulation to reduce sparsity in the high-dimensional latent space.\n- I find the connection between VAE training and physical systems, such as spin glasses, particularly valuable, as it provides a novel perspective for understanding the model's training dynamics." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose expressing the latent variable of a standard VAE in hyperspherical coordinates, reformulating the KL term of the ELBO loss accordingly, to reduce the sparsity in high-dimensional latent spaces and improve the generative performance of the model. Their approach draws on a parallel between high-dimensional spaces in statistical physics, specifically spin glasses, and the training dynamics of a VAE." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The authors should revise the references (e.g., replace arXiv preprints with published versions where available, add access dates for blog references, and consider replacing Wikipedia links with more reliable sources). Additionally, some typos were noted in the main text, and Figures 4 and 5 appear in low resolution, making the text difficult to read.\n- I think the experimental section could be strengthened. Although the primary aim of this work is to improve the model's generative performance, the authors present generated samples only on MNIST, which is a simple dataset. Also, they do not compare the generative performance of the method with any other baseline (in the introduction they mention methods that improve generative performance by using more flexible priors but do not show if the results achieved are comparable).\n- The paper lacks a concluding discussion and does not explore potential limitations of the method or directions for future research, which I believe would add significant value." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- As mentioned in the weaknesses section, a VAE usually learns in a polarised regime when $\\beta$ is sufficiently high. In this setting, the latent representations contain two kinds of variables: active and passive. The passive variables are kept as close to the prior as possible and used to lower the KL divergence, while the active variables contain the information needed for reconstruction (or further use in downstream tasks). Active variables typically do not follow the prior as the KL is kept low by the passive variables. Instead, they have a very low $\\\\sigma$ such that during the reparametrisation trick, $z \\approx \\\\mu$. One would usually remove passive variables for downstream tasks, only keeping the small subset of variables containing some information [8]. I wonder if keeping only active variables would change the sphere projection to something more akin to what is obtained with hyperspherical coordinates?\n- What is the impact of using different values of $a_{\\mu, k}$ (keeping $a_{\\mu, k} \\neq 0$ of course)? Was there a specific reason to choose $a_{\\mu, k}=1$?\n\nReferences\n=========\n- [1] Kingma, D. P. and Welling, M. (2014). Auto-Encoding Variational Bayes. In International Conference on Learning Representations, vol. 2.\n- [2] Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Shakir, M. and Lerchner, A. (2017). $\\\\beta$-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In International Conference on Learning Representations, vol. 5.\n- [3] Rolinek, M., Zietlow, D. and Martius, G. (2019). Variational Autoencoders Pursue PCA Directions (by Accident). In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).\n- [4] Dai, B., Wang, Y., Aston, J., Hua, G. and Wipf, D. (2018). Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models. Journal of Machine Learning Research, 19(41), pp. 1–42\n- [5] Lucas, J., Tucker, G., Grosse, R. B. and Norouzi, M. (2019a). Don’t Blame the ELBO! A linear VAE Perspective on Posterior Collapse. In Advances in Neural Information Processing Systems, vol. 32.\n- [6] Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R. and Bengio, S. (2016). Generating Sentences from a Continuous Space. In Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning.\n- [7] Rezende, D. J., Mohamed, S. and Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proceedings of the 31st International Conference on Machine Learning, Proceedings of Machine\nLearning Research, vol. 32.\n- [8] Bonheme, Lisa, and Marek Grzes. \"Be more active! understanding the differences between mean and sampled representations of variational autoencoders.\" The Journal of Machine Learning Research 24.1 (2023): 15423-15452." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "Originality\n========\n- I like the creative approach of solving the issue of poor image quality generation by applying a solution to a similar problem from physics.\nThis is quite different from previous publications about hyperspherical VAEs, where the main motivation (as far as I know) was to provide a better prior than the standard multivariate Gaussian.\n- The proposed model also differs from others in the hyperspherical VAE literature. Usually, changing the prior and posterior distribution can be complex as the reparametrisation trick and the KL divergence need to be updated accordingly. Here, the proposed solution is an elegant change of coordinates done after the reparametrisation trick during the KL divergence computation, making it easy to implement and intuitive to understand.\n\nQuality/Clarity\n===========\n- The section on spin glasses is well vulgarised, intuitive, and reads very well for non-physicians.\n- The paper is generally well-written and easy to follow.\n\nSignificance\n=========\n- Given the simplicity of the implementation, practitioners looking for better image generation quality with VAEs could easily adopt the proposed model.\n- The explanation of why the generation is bad at inference time is interesting for the research community working on the learning dynamics of VAEs." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the poor image generation quality of VAEs at inference time when the latent representation is high dimensional. After hypothesising, based on spin glasses theory from statistical physics, that the issue comes from the sparsity of high-dimensional spaces. Based on this, they propose to apply a mechanism akin to the quenching process used to reduce the entropy of such systems. Practically, this is done by implementing a change of coordinates from Euclidean to hyperspherical during the KL divergence computation and setting the priors of each dimension of these new coordinates such that the latent samples are pushed away from highly entropic regions. This results in an improved generation quality for MNIST while keeping latents that are interpretable enough to perform clustering." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "While I really like this paper's approach, I have some concerns about its empirical soundness and found several mistakes in the given equations (see major comments below). Clarifying some aspects of the paper would also strengthen it (see major and minor comments below). If these concerns were addressed, I would happily raise my score.\n\nMajor comments\n=============\n\nExperimental soundness\n---------------------------------\n- The experiment compares the results of $\\\\beta$-VAEs with change of coordinates + annealing with the results obtained with $\\beta$-VAEs without annealing. As a result it is not possible to see if the improved generation comes from the annealing or the change of coordinates. Additional results with $\\\\beta$-VAEs using the same annealing schedule would be needed to ensure that the improvement is really due to the change of coordinates.\n- While the experiment is partially done on CIFAR 10, it is hard to assess how several results generalise. For example, a comparison of the classification accuracy, examples of images generated, and a comparison of the projection into spheres using both models would be nice to have on CIFAR 10 as well.\n\nMathematical soundness\n----------------------------------\nIn section 1.1, several equations were incorrect.\n- In Eq. (1) $KLD(q_{\\\\phi}(z) || p_{\\\\theta}(z))$ should be $KLD(q_{\\\\phi}(z|x) || p_{\\\\theta}(z))$\n- In Eq. (2) the given formula is not for $KLD(z,\\\\epsilon)$ but for $-KLD(z,\\ \\epsilon)$. Furthermore, subscripts from the summations are missing. I would suggest either removing the second summation and using a matrix form like $KLD(z,\\\\epsilon) = \\\\frac{1}{2} \\sum^{N_b} \\\\bigl(Tr(\\\\sigma) + || \\\\mu||^2_2 - \\\\log det(\\\\sigma) - n\\\\bigr)$, or rewriting it with subscripts as $KLD(z,\\\\epsilon) = \\\\frac{1}{2} \\sum^{N_b} \\sum_{k=1}^n \\\\bigl(\\\\sigma_i^2 + \\\\mu_i^2 - \\\\log(\\\\sigma_i^2) -1 \\\\bigr)$.\n- In Eq. (3), this is not the ELBO $\\\\mathcal{L}$ from Eq. (1) but its negative approximation $- \\\\tilde{\\\\mathcal{L}}$ which is minimised by the VAE. While the original formulation of VAEs by [1] does not contain a $\\\\beta$ term and is equivalent to setting $\\\\beta=1$, it would be interesting to briefly discuss what is the impact $\\\\beta$ when $\\\\beta > 1$ and when $\\\\beta < 1$. Indeed, the motivation for both settings is very different: the first is to provide \"disentangled representations\" [2] and to force the VAE to learn in a polarised regime (a.k.a selective posterior collapse), which is akin to a PCA-like behaviour [3,4,5], while the second aims at mitigating posterior collapse and is often used together with annealing [6]. Thus, the choice of $\\\\beta$ has a practical impact on the proposed experiment (see further discussion on the questions part below).\n\nClarity\n---------\n- To facilitate the understanding of the paper, it would be great to have the derivations from Eq. (4) to Eq. (5) and from Eq (8) to Eq (9) in appendix.\n\n\nMinor comments\n=============\n\nMathematical notation\n------------------------------\nThe current notation is sometimes confusing. For example, l. 77 $\\\\mathcal{N}(z; \\\\mu, \\\\sigma)$ reads as \"the univariate Gaussian with mean $\\\\mu$ and standard deviation $\\\\sigma$\" while it is in fact a multivariate Gaussian. A suggestion to improve this is to use a different notation for numbers, vectors, and matrices, following, for example the notation suggested in the math_commands.tex file of the ICLR template.\n\nClarity\n---------\n- l. 478, I found the sentence \"the quality of AE and VAE [...]\" confusing as AEs are not discussed anywhere else in the paper and are not used in the experiment. I would suggest removing the part about AE to make the argument clearer.\n- I struggle to see what 32% of computation time of the KLD represent in term of additional training time. It would be easier to see with an average run time with and without change of coordinates over n seeds and k epochs. Furthermore, if this increases with the number of dimensions, an estimate of the increase rate using big O notation would be very useful for practitioners to assess whether this implementation is suitable to their needs.\n- The seminal papers on VAEs are [1,7] which are different from the ones references. I would suggest updating this.\n\nTypos\n--------\n- l. 91 weighs -> weights\n- l. 100 teh -> \"that the\" ?\n- l. 133 there's -> there is\n- l. 133 have -> has\n- l. 157 it's -> it is\n- l. 422 gven -> given\n- KL divergence is inconsistently refered to as \"KL divergence\" and \"KLD\" in the paper.\n- l.591-592, the title of Higgins et al. is capitalised while others are not." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose to convert the latent variables of a VAE to hyperspherical coordinates. This allows to move the latent vectors to a small island of the hypersphere, reducing sparsity. We showed that this improves the generation quality of a VAE." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024is,\ntitle={Is the sparsity of high dimensional spaces the reason why {VAE}s are poor generative models?},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4xEACJ2fFn},\nnote={under review}\n}" }, "abstract": { "value": "Variational autoencoders (VAE) encode data into lower dimension latent vectors before decoding those vectors back to data. Once trained, decoding a random latent vector usually does not produce meaningful data, at least when the latent space has more than a dozen dimensions. In this paper, we investigate this issue drawing insight from high dimensional physical systems such as spin-glasses, which exhibit a phase transition from a high entropy random configuration to a lower energy and more organised state when cooled quickly in the presence of a magnetic field. The latent of a standard VAE is by definition close to a uniform distribution on a hypersphere, and thus similar to the high entropy spin-glass state. We propose to formulate the latent variables of a VAE using hyperspherical coordinates, which allows to compress the latent vectors towards an island on the hypersphere, thereby reducing the latent sparsity, analogous to a quenched spin-glass. We show that this is feasible with modest computational increase and that it improves the generation ability of the VAE." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "variational autoencoder", "generative model", "high dimensional statistics", "spin glass", "latent space", "hyperspherical coordinates" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/5d619309dbe0bd30676f1d978912520b27918362.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Is the sparsity of high dimensional spaces the reason why VAEs are poor generative models?" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "No questions." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper is well-written, easy to follow, and its results are clear and novel. The theoretical results stand out for their depth and clarity, as do the empirical results. The support of images is quite helpful when reading through some mathematical arguments or proofs of theoretical results." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors present a deep and novel analysis of the loss landscape and a solution in the context of regularized neural networks. They also show that the topology of the global optima undergoes a phase transition as the width of the network changes." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Perhaps a deeper discussion on the topological implications of their results would be beneficial." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Is there a way to bound or estimate the critical widths $m^*$ and $M^*$ in practice, for instance on real datasets? \n2. In line 186: What does $h$ refer to in $\\text{diag}[1 (Xh \\geq 0)]$ ?\n3. It is not very clear to me what lines 225-226 mean. Could you perhaps rephrase it? (That $\\mathcal{P}^*_{\\nu^*}$ does depend on $\\nu^*$, but that the specific choice of it does not matter.)\n4. Figure 2 bottom: The axis labels are missing and it is not very clear to me what the red and blue lines are supposed to represent.\n5. In line 351 the author mention three interpolation problems of interest, but only discuss one problem on the minimum-norm interpolation problem. What are the other two interpolation problems and can you also extend your results to these problems?\n6. The paper describes a path of nonincreasing loss that connects local to global minima. Could this insight be incorporated into practical training algorithms, such as initializing weights or guiding optimizers in large-scale training?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- I found the \"staircase of connectivity\" very insightful, particularly how the connectivity properties of the optimal solutions are connected to critical widths $m^*$ and $M^*$. This finding explains how increasing the number of neurons affects the connectedness of optimal sets, and makes the observation of mode connectivity [Garipov et al. 2018] more precise. \n- The paper generalizes its findings also to vector-valued networks and deep networks with skip connection, which provides a broader framework that can be applied across different architectures." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this work the authors analyze multiple aspects of the loss landscape of regularized two-layer neural networks with scalar output, including the structure of stationary points, the connectivity of optimal solutions and the non uniqueness of optimal solutions. The main proof strategy is to translate the problem into an equivalent convex problem and characterize its solution set through its dual form. \nThe authors show that the topology of the global optima goes through a phase transition as a function of the hidden layer width, which they term the staircase of connectivity. \nThis result is extended later to networks with vector-valued outputs, and parallel deep networks of depth 3." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- I found the theoretical results, for instance on the staircase of connectivity, hard to interpret in practice and would benefit from more accessible explanations. While the toy example in Example 1 illustrates the concept, the absence of labels in Figure 2, as well as the notation-heavy formulation makes it difficult for readers to grasp the results intuitively. \n- Although the toy examples are helpful, the paper lacks actual empirical validation of the theoretic results. I think it would add credibility to this work, if the staircase of connectivity concept would also be tested on actual neural network architectures trained on real data. It would be interesting to see how these results scale with different data distributions and larger models. \n- Overall I found the work quite difficult to read due to the dense mathematical formalism. I also feel like the section on notations should not be in the appendix, but should - at least in a shortened version - be included in the main paper." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "I wonder if the authors have any comment regarding the weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The authors apply the new technique of convex duality to problems of connectivity and minimal-norm interpolation, which have been studied previously using other methods. This approach yields both generalizations of existing results and new insights into these problems. Overall, I believe this paper is a strong demonstration of how convex duality can be leveraged in the theoretical study of machine learning. The abstract concepts are clarified through figures and examples." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors apply convex duality for two-layer ReLU networks to study mode connectivity and unique minimal-norm interpolator problems, while also working to generalize this framework. Specifically, the authors have:\n\n* identified the staircase of connectivity that describes how connectivity evolves with width;\n* constructed none-unique minimal-norm interpolator by breaking the uniqueness conditions;\n* generalized the optimal polytope to the general cone-constrained group LASSO problem and applied it to more complicated architectures." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I have some concerns with the presentation of this work. Specifically:\n\n* If I understand correctly, the convex duality only applies to ReLU networks. This is not emphasized.\n* I found Section 2 difficult to follow without prior knowledge of Pilanci & Ergen (2020). The relations between (1), (2), and (3) are mentioned but not explained (When do they have the same loss value? How do the solutions relate to each other?) Dimensions of $X$ and $y$ are not mentioned. $D_i$ is not explained.\n* In Figure 1, is each red point truly a unique solution, or does it represent solutions equivalent under permutation (p-unique)? If they are p-unique solutions, readers may get the wrong impression. \n* The lower half of Figure 2 is not explained." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. A general question is about the scope of the techniques in this paper. It seems that the techniques only apply to two-layer ReLU networks, since the problem can be equivalently written as a convex problem with regularization. It is not applicable to other activation functions and seems hard to generalize to multi-layer cases. Thus, could you elaborate more on the universality of the techniques?\n\n2. The results in this paper require the number of neurons $m \\geq m_*$. As far as I understand, $m_*$ is the minimal number of neurons needed to achieve the optimal model. I'm wondering what would happen if $m<m_*$? Also, in general, what is the scaling of $m_*$ depending on $n,d$? \n\n3. The results in Theorem 2 consider the connectivity of the optimal solution set, which is equivalent to the connectivity of a path with $0$ perturbation. What about the case that allow $\\epsilon$-pertubation along the path? Is the techniques still applicable?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper develops a general framework to characterize the global optimum of the regularized ReLU network via convex duality. From my understanding, the key contribution of this convex duality framework in Theorem 1 is that it allows one to characterize the \"direction\" of the weights separately in the regularized case, which is then useful for characterizing the global optimum. I believe this contribution is novel and solid. \n\n2. I think the framework of characterizing the global optimal is quite general even though it is restricted to ReLU network. In particular, it do not require large over-parameterization, special scaling, or special data distributions. Thus, I believe the results can be applied to other more specific settings and is potentially useful for characterizing other properties besides the connectivity of the solutions." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the loss landscape of ReLU networks with $L_2$-regularization. The authors first study the canonical case of a two-layer network with scalar output, and characterize the connectivity of the solution for different number of neurons. Then, the authors extend the results to a more general class of problems, including: minimal norm interpolation, vector-valued ReLU network, and parallel deep neural network." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.Although I believe this paper has a solid contribution, I found there's a few part I don't understand the significance: \n- I think I understand the contributions in section 3.1 and 3.2, however, I'm not sure about In section 3.3, the authors showed that for a class of data set with dimension $=1$ that satisfies certain conditions, if the network do not have skip connection, then there are infinitely many minimal norm interpolators (which is a connected set ). I'm not sure the significance of these results, since (1) it is for a special construction of dataset. (2) it might be that those infinitely many minimal norm interpolators behave qualitatively almost the same, for example, the radius of the solution set is small. Could you discuss more on the significance of the results?\n\n- In section 4, I understand the contribution of generalizing it to a vector-valued function. However, I'm not sure the significance of the results in Theorem 4. Since anyway you fixed all the other layers but only keep two consecutive layers, and technically I didn't see any difference from a two-layer network. Could you discuss more on the significance of the results?\n\n2. One main issue of the paper is the writing, especially the main part of the paper. I check the appendices, and it is much more readable. So I suggest the authors consider rearranging the content. To name a few issues\\typos that confuse me when reading the main part: \n\n - Line 215: and 216, what is the definition of $\\mathcal{S}_i$?\n - Line 223: what the definition of \"optimal model fit\", what is $u_i^*, v_i^*$, and why it is unique?\n - Line 232: the triangle inequality is reversed. Also could you be more specific about the discussion between Liine 229-232?\n - The statement of Proposition 1: First, you use $v_{i,1}$ to denote the first entry of a vector, could you specify this? Also, you define $s_k = \\sum_{i=1}^k v_{n-i+1},$ but also require $||s_k|| =1, s_n = [0,1]^\\top$, could you discuss the existence of such construction? \n - In equation (7), could you specify the dimension of the variables?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Can the authors proofread again the manuscript to eliminate typos, unclear sentences and missing notation?\nCan they make the presentation of the result more readable, including relevant results from the Appendix?\nCan they integrate the relevant existing literature in the Related Work section?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper has original content, and bridges together several concepts proposed in the literature on the topic. The method of analysis is rigorous and it gives a solid contribution to the field." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This manuscript proposes a characterization of the topology of the global optima in the loss landscape, focusing on regularized two-layers neural networks with free skip-connections with a partial extension to deep neural networks. The authors provide a characterization of the optimal set in terms of the width of the hidden layer, which determines a so-called \"staircase of connectivity\" when such a width occurs in critical values and phase transitions. The authors study the uniqueness of the minimum-norm interpolator, highlighting necessary guarantees (such as the free ski connections, bias in the training problem and unidmiensional data). An experimental study integrates the theoretical findings." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The manuscript presents some unclear sentences (e.g. line 198-199). There is a clear math error at line 232 (the triangle inequality holds with the reverse inequality; to be candid, I am quite sure it is a typo) and some symbols are not defined at all, not even in the Appendix (e.g. the symbol P, that occurs very often through the entire manuscript). \nThere are many references to results listed in the Appendix; if relevant, I think it might be better to put them in the main manuscript.\nAn important reference to the characterization of loss landscapes over neural networks with regularization terms and/or skip connections is missing, also because it gives a theoretical hint on the low importance of skip connections [1].\n\n[1] Bucarelli, M. S., D’Inverno, G. A., Bianchini, M., Scarselli, F., & Silvestri, F. (2024). A topological description of loss surfaces based on Betti Numbers. Neural Networks, 106465." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We investigate the loss landscape and topology of the optimal set of neural networks using convex duality." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024exploring,\ntitle={Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4xWQS2z77v},\nnote={under review}\n}" }, "abstract": { "value": "We discuss several aspects of the loss landscape of regularized neural networks: the structure of stationary points, connectivity of optimal solutions, path with non-increasing loss to arbitrary global optimum, and the nonuniqueness of optimal solutions, by casting the problem into an equivalent convex problem and considering its dual. Starting from two-layer neural networks with scalar output, we first characterize the solution set of the convex problem using its dual and further characterize all stationary points. With the characterization, we show that the topology of the global optima goes through a phase transition as the width of the network changes, and construct counterexamples where the problem may have a continuum of optimal solutions. Finally, we show that the solution set characterization and connectivity results can be extended to different architectures, including two layer vector-valued neural networks and parallel three-layer neural networks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Convex duality", "Machine Learning Theory", "Loss Landscape", "Optimal Sets" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/e2aa7c3ffd9df4b407f85c7290421cfd69aa88b7.pdf" }, "presentation": null, "primary_area": { "value": "learning theory" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/f41e0eed403477ede6e6aad5ef92572362054908.zip" }, "title": { "value": "Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "see above" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper considers an important problem: how diffusion models can be used to solve complex inverse problems, such as giving a segmented image to reproduce the underlying image. The paper acknowledges a known limitation on one assumption that prior work makes on the distribution of the data (which allows to use the Tweedie's formula), and proposes to address it by consistency models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes using consistency models (CMs) to solve inverse problems with diffusion models. In diffusion inverse problems, common approaches need to go from xt to x0 at every iteration to be able to compute a measurement-guided gradient with respect to the measurement y from x0. The majority uses the expectation from Tweedie's formula to compute x0 from tx, which may not result in a good example for complex multi-modal distribution. The paper proposes to replace this step with a CM. They show improvement upon prior work." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I find the contribution of the contribution incremental, and the presentation can be improved. The main weaknesses are as follows: the mathematical formulation is confusing and the approach to training the CM is not fully fair compared to the baselines.\n\n1. The paper trains a CM to be used for diffusion-based inverse solvers. It's not clear why such mathematical details (some are not fully proper) are included, which I do not think is the main contribution of the paper. It's unclear why in (1) the authors formulate the Markov chain following a VE formulation. Could the author explain this choice? The conventional Markov chain for diffusion models is the popular one based on VP, which has a different mean and variance such that the structure is destroyed, but the energy of the process remains the same.\n\n2. Solving inverse problems with diffusion mostly occurs in the regime where the diffusion model is trained unconditionally without the knowledge of the measurement operator f(.) (see the DPS used for vision problems such as deblurring). However, the paper in Section 3.4 discusses that the CM is overfitting to f(.) and proposes an approach to make the framework robust. So this brings the following: the CM going from xt to x0 seems to be trained with the knowledge of the measurement. If f(.) is involved during the training, then the trained framework is not general anymore (it's problem specific). Hence, the comparison of the proposed framework to models such as DPS, where the model is not trained based on the measurement operator, is not fair. Please provide more information and clarity if this is not the case. The fair comparison would be a scenario where both methods are trained a similar condition (e.g., not having the knowledge of the forward operator).\n\nHere are some questions concerning this:\n\n- Is CM trained with knowledge of f(.), or if this is a misunderstanding?\n- If the CM is trained with f(.), please explain and justify comparing it to methods like DPS that don't use this information?\n\nMore comments are below:\n\n\nLack of thorough literature\n\n- The limitations of DIS are not fully explained in the intro. Indeed, the mean-based approximation is one challenge. A few others are related to whether methods such as DPS are doing posterior sampling or using the measurement to guide the process onto likely solutions (see [1]).\n\n\nThe paper needs improvement in presentation. Here are a few examples\n\n- While the notations such as Xt and X0 are known to the reader with knowledge of diffusion models, these are used in the abstract and intro without introducing them. Hence, I suggest re-writing the abstract without these notations and introducing the diffusion in the introduction before using x0, xt, etc.\n\n- Consistency models are not defined and introduced in the intro, but the authors explain that they are used to improve performance. I suggest the authors to provide a brief definition or explanation of consistency models in the introduction.\n\n- How the results are generated for Table 1; this appears abruptly without proper explanation. I suggest to include a brief explanation of the methodology used to generate the results in Table 1.\n\nSome terms within the manuscript are not precise and clear. Please provide clarifications.\n\n- Section 1: with \"neural network operators\"? Does this refer to the measurement operator or the score function of the diffusion? I suggest to say \"measurement operators\" instead of \"operators\". Please clarify \"neural network operators\"?\n\n\n[1] Wu, Z., Sun, Y., Chen, Y., Zhang, B., Yue, Y., & Bouman, K. L. (2024). Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors. arXiv preprint arXiv:2405.18782." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. More DIS baselines are desired such as CoSIGN [2], DDNM [3]\n\nI am open to change my rating if authors could address my concerns (how clearly a CM model could benefits with inverse problem solving comparing to strong baselines like DDNM)" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Easy to follow\n2. The idea of using CM to approximate the PF-ODE solution is interesting" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents an interesting approach for posterior sampling using $p(x_0|x_t)$ being approximated via consistency model. Results show improvement over baselines such as DPS" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Using CM over the existing inverse problem solving adds a lot of computational burden, while the benefit is not clearly visible. Even though the $x_0|x_t$ may be off the manifold of ground truth image distribution, this does not imply a sacrifice in reconstruction quality as demonstrated in this work [1]. The quantitative results do not show a significant improvement over DPS.\n2. CM itself can be used as a good prior for solving inverse problems: see this work [2]. This paper needs to compare with more recent works in inverse problem solving\n3. More DIS baselines are desired such as DDNM [3]\n\n\n\n\n[1]. Wang, Hengkang, et al. \"DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models.\" arXiv preprint arXiv:2405.16749 (2024). NeurIPS 2024\n\n[2]. Zhao, Jiankun, Bowen Song, and Liyue Shen. \"CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems.\" ECCV 2024\n\n[3]. Wang, Yinhuai, Jiwen Yu, and Jian Zhang. \"Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model.\" The Eleventh International Conference on Learning Representations." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "The presentation of Proposition 3.3 is a bit misleading. The authors should state in the assumption the condition on $\\sigma$ that they use in the proof in order to get a lowerbound independent of the dimension, or remove this claim after the proposition." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The idea is quite original wrt to the literature. The paper is quite illustrated and clear. The experiments are interesting and extensive; the authors compare to many existing methods, on both pixel space and latent space diffusion." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper is about solving inverse problems using a pre-trained denoising diffusion prior. Posterior sampling with diffusion models requires an estimate of the score $\\nabla _{x_t} \\log p_t(x_t) + \\nabla _{x_t} \\log \\int p(y | x_0) p _{0|t}(x_0 | x_t) d x_0$. While the first term is estimated using the pre-trained score, the second term is usually very difficult to estimate accurately. A common approximation used in the literature involves using $\\nabla _{x_t} \\log p(y | E[X_0 | X_t = x_t])$ where $E[X_0 | X_t = x_t]$ can also be estimated using the pre-trained score via Tweedie's formula. This approximation results in many efficiencies well documented in the literature and this paper tries to circumvent them using by using a sample from the PF-ODE as a replacement. Specifically, the authors use consistency models to speed up the process of sampling from the PF-ODE and ensuring that the differentiation is not costly." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The most obvious weakness of the method is the use of consistency models since these are quite difficult to train and pre-trained CMs are not widely available. \n\n- In my opinion the justification for the method is rather weak. The paper argues that using a sample from the PF-ODE is valid because the sample has zero density and that furthermore, for the Gaussian mixture example considered, the PF-ODE sample has non-zero density under the posterior $p(x_0 | x_t)$ with high probability. At the same time it is also easy to find examples of a likelihood function $p(y|x_0)$ such $\\int p(y|x_0) p(x_0 | x_t) d x_0 > 0$ for all $x_t$ but $p(y|\\Phi(t, x_t)) = 0$ for $x_t$ in a set of positive Lebesgue measure. As a result, I'm not totally convinced that the argument is strong." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "The presented manuscript often states likelihood terms as $p_\\theta(y|x)$. Please elaborate on why it contains theta, as the likelihood in general is not a learned function with the same parameters as the learned data distribution $p_\\theta(x)$.\n\nFurthermore, I would be interested as to which extend the PF-ODE solution differs from the MAP solution?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The idea of using CMs in the process of inverse problems/posterior approximation seems novel and interesting.\n\nThe proposed method outperforms baseline approaches, particularly when compared to straightforward extension" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose a new approach using CMs to generate realistic image samples in Diffusion Inverse Solvers, improving the application of complex, non-linear neural network operators like those in semantic segmentation, room layout estimation, image captioning, and image classification. Unlike traditional methods that produce low probability images, the incorporation of CMs is expected to maintain sample realism, resulting in more accurate posterior approximations, particularly when neural network-based operators are involved." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although being interesting, the novelty of the presented work is marginally above acceptation threshold since the only contribution seems to be the use of CMs in order to compute $x_0$ from $x_t$.\n\nThe manuscript could benefit from improved clarity and organization, as certain sections are challenging to follow. See further remarks.\n\nFurther remarks: \n\nIn the presented algorithm, the updates are stated as $\\zeta_t \\Delta\\left(f(x_0 \\mid t), y\\right)$, where $\\Delta$ is defined as some distance. The update of $x_t$ however should be the gradient of that distance.\n\nIn order to enhance the readability of the work, the authors should think about introducing a clear distinction between the posterior $p(x_0|x_t)$ and the posterior $p(x|y)$, given y is the observation.\n\nThe abbreviation DPS (Diffusion posterior sampling) is used but never introduced." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose to use consistency model instead of posterior mean to approximate posterior samples during diffusion posterior sampling." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024consistency,\ntitle={Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4xbwWerxvZ},\nnote={under review}\n}" }, "abstract": { "value": "Diffusion Inverse Solvers (DIS) are designed to sample from the conditional distribution $p_{\\theta}(X_0|y)$, with a pre-trained diffusion model $p_{\\theta}(X_0)$, an operator $f(.)$, and a measurement $y=f(x'\\_0)$ derived from an unknown image $x'\\_0$. Existing DIS estimate the conditional score function by evaluating $f(.)$ with an approximated posterior sample drawn from $p\\_{\\theta}(X_0|X_t)$. However, most prior approximations rely on the posterior means, which may not lie in the support of the image distribution and diverge from the appearance of genuine images. Such out-of-support samples may significantly degrade the performance of the operator $f(.)$, particularly when it is a neural network. In this paper, we introduces a novel approach for posterior approximation that guarantees to generate valid samples within the support of the image distribution, and also enhances the compatibility with neural network-based operators $f(.)$. We first demonstrate that the solution of the Probability Flow Ordinary Differential Equation (PF-ODE) with an initial value $x_t$ yields an effective posterior sample of $p_{\\theta}(X_0|X_t=x_t)$ with high probability. Based on this observation, we adopt the Consistency Model (CM), which is distilled from PF-ODE, for posterior sampling. Through extensive experiments, we show that our proposed method for posterior sample approximation substantially enhance the effectiveness of DIS for neural network operators $f(.)$ (e.g., in semantic segmentation). The source code is provided in the supplementary material." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Diffusion model", "Inverse problem" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/736044c787692a45ffaa0a19841cd9e3977f2f8b.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/3cf1363286ff310a24a3391fe90f153f7212aea6.zip" }, "title": { "value": "Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "We are most grateful for the reviewer's thorough analysis and recognition of our work's quality.\n\nRegarding the Pareto Front filtering (lines 299-304), this occurs after architecture generation. For each architecture, we compute the parameters, MACs and inference latency, whilst using the predictor to estimate accuracy. From these Pareto fronts, we identify three configurations per secondary metric: the most efficient architecture (lowest metric), the most balanced (optimal accuracy/metric trade-off), and the most accurate (highest predicted accuracy). This approach provides practitioners with clear options suited to different deployment scenarios.\n\nThe foundational works - MetaD2A (Lee et al., ICLR 2021), TNAS (Shala et al., ICLR 2023) and DiffusionNAG (An et al., ICLR 2024) - were all published at ICLR and establish MobileNetV3 and NASBench201 as standard benchmarks. Whilst POMONAG builds upon this established research trajectory, we have substantially expanded the validation across a broader range of datasets to demonstrate wider applicability.\n\nOur contribution extends well beyond enhancing DiffusionNAG. A primary innovation is the formulation of Many-Objective Reverse Diffusion Guidance, which elegantly balances four distinct gradients during generation. The optimisation of these gradients presented unique challenges: the scaling factors operate across vastly different scales, whilst maintaining convergence and architectural quality. We addressed this through a novel two-phase approach (lines 324-333) optimised via Hyperband pruning.\n\nThe Performance Predictors underwent significant redesign, yielding marked improvements in Spearman correlation (from 0.687 to 0.855). The expanded Meta-Dataset properly supports multi-objective optimisation, whilst our Pareto-optimal filtering identifies three practical configurations (Acc/Bal/Eff) suited to different deployment contexts. The empirical results validate these contributions conclusively: POMONAG surpasses DiffusionNAG in both accuracy (+4.06% on NASBench201) and efficiency metrics, with remarkable reductions in parameters (90%) and MACs (93%).\n\nWe might also note that POMONAG achieves these improvements whilst requiring only a single architecture to be trained per dataset, significantly reducing computational overhead compared to prior approaches.\n\nWe trust these clarifications address the points raised and demonstrate the substantial nature of our contributions. We are grateful for the reviewer's careful consideration and hope these explanations enable a fuller appreciation of the work's merit." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "Moreover, I have the following questions:\n\n- Can the authors provide more theoretical or empirical justification for the scaling factors in the Pareto Front Stretching process? How sensitive is the model to these values?\n\n- Can the authors provide more detail on the architecture sampling process, dataset splits, and hyperparameter tuning methods used in the experiments? This is particularly important for the performance predictors." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The motivation to extend DiffusionNAG to a many-objective setting is valid and POMONAG does so by incorporating both accuracy and efficiency metrics like latency and MACs, which are critical for resource-constrained environments. The paper provides extensive experimental comparisons with DiffusionNAG, including evaluations across multiple datasets and search spaces, which helps demonstrate the general applicability of POMONAG.\nBalancing the different objectives being optimized is also very important in my opinion. The authors do so by proposing a pareto front filtering and stretching subroutine." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces POMONAG, an extension to DiffusionNAG that applies a many-objective diffusion model to optimize neural architecture generation for many-objective optimization. By incorporating additional performance predictors for hardware efficiency metrics such as number of parameters, multiply-accumulate operations (MACs), and inference latency, POMONAG aims to provide a more balanced approach to architecture optimization across accuracy and computational efficiency. Experiments validate POMONAG’s efficacy on two major CNN search spaces (NASBench201 and MobileNetV3)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I have the following main concerns related to this submission, which I believe were crucial in the final decision:\n\n- **Incremental Contributions**: Although POMONAG claims to extend DiffusionNAG’s capabilities by addressing more objectives, the modifications appear incremental and lack substantial theoretical advancement. More specifically, I see the adaptation of diffusion models to accommodate multiple objectives, as described in section 3.1, more as a technical modification rather than a novel conceptual framework. I would recommend the authors to reiterate over their methodology and pinpoint the main contributions of their approach.\n\n- **Experimental Evaluation**: The benchmarks that POMONAG was evaluated contain only CNN spaces. It would be beneficial for the paper if the authors would demonstrate the efficacy of POMONAG in Transformer search spaces, such as the one from HW-GPT-Bench [1]. Most importantly, in the multi-(many-)objective experiments, the proposed method is not compared to any baseline. I would recommend the authors to add baselines in their experimental evaluation and report hypervolume indicator together with the individual objective values, as well as the search time. Ultimately, I would also be interested in visualizing the pareto front plots in the main paper. As for baselines, you can find a non-exhaustive list of simple ones in SyneTune (https://syne-tune.readthedocs.io/en/latest/getting_started.html#supported-multi-objective-optimization-methods). Finally, the experiments lack a thorough ablation study that demonstrates the impact of POMONAG’s unique contributions independently of DiffusionNAG’s foundational structure. \n\n- **Clarity and Presentation**: The paper seems to have a somehow fragmented structure, making it challenging for readers to follow the main contributions and crucial take-away points. Equations are not thoroughly explained, and there is a heavy reliance on citations from DiffusionNAG rather than a detailed elaboration of POMONAG itself, making the paper not self-contained. One major point here, which I have also pointed out to the AC, is that the authors have used a smaller font size starting from page 4. The guidelines clearly state that the maximum page limit is 10 and that means 10 pages with the default font size, not a smaller one. I suggest the authors that in future submissions they adhere to the submission guidelines.\n\n\n-- References --\n\n[1] Sukthanker et al. HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models. In NeurIPS 2024 DBT" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "N/A" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See above as the questions are mostly addressing the weakness of this paper." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The motivation of this study, introducing multi-objective evaluation in NAS, is commendable as a task in reality is often not just about accuracy. Other metrics should be considered simultaneously as well.\n\nThe writing is easy to follow. \n\nIt is nice to see equations with highlights of different colours." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This study improved DiffusionNAG by introducing a multi-objective approach which modifies DiffusionNAG's reverse diffusion process as a reverse diffusion guidance process. Other than accuracy, #params, MACs and inference latency are also considered in the multi-objective metrics. The proposed method POMONAG has been tested on NASBench201 and MobileNetV3 with 15 image classification tasks, showing better performance than DiffusionNAG and a series of other methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**First of all**, the work claims to be on Pareto multiobjective search for architectures. However, that point is not obvious from the paper. \n* What are the benefits of using the proposed POMONAG? \n* How can a Pareto front be generated and utilized? Need to explicitly demonstrate how POMONAG generates and utilizes Pareto fronts.\n* How can users select architectures from the Pareto front according to their needs or under different circumstances? Show examples of such selection based on different priorities, for example prioritizing small-size architectures for portable devices or focusing on latency reduction etc. \n* It seems non-dominated sorting is absent. Explain how non-dominated sorting is incorporated or can be incorporated in POMONAG.\n* In its current form, the paper reads like a combination or integration of single-objective evaluations rather than a multi-objective evaluation. The equation of POMONAG at Line 209/210 is a linear combination of four objectives. Please clarify if the linear combination of objectives is intended as a scalarization approach. If so, discuss its limitations.\n--- \n**Secondly**, the performance of POMONAG appears better than DiffusionNAG and other methods shown in the paper. However many SOTA methods, especially zero proxy methods are missing. Their reported performance is similar or even better, for example, SWAP-NAS by Peng et al, ICLR'24, ZiCo by Li et al, ICLR'23, MeCo by Jiang et al, NeurIPS'23.\n* Include a comparison with these SOTA methods. If a direct comparison is not possible, explain why and discuss the limitations of the current evaluation.\n* Discuss how POMONAG's approach differs from or improves upon zero-proxy methods. \n\n--- \n**Thirdly**, the computational cost aspect of POMONAG is weak. The section \"Generation and Training Time\" should be better presented. The method requires a diffusion generation phase which takes extra time. That itself is a disadvantage. Also timewise, POMONAG cannot claim superiority as recent methods mentioned earlier are faster.\n* Present a detailed table comparing computational costs (including generation and training time) of POMONAG with other methods, including these zero proxy methods mentioned above. Seemingly these methods are faster. If POMONAG is indeed slower, discuss potential optimization strategies.\n* Discuss the trade-offs between the additional diffusion generation phase and the method's performance gains. Justify why the additional computational cost might be worthwhile.\n--- \n**Other points:**\n \nThe link at Line 091 is showing. Also, including the code and dataset would be helpful for the assessment.\n\n--- \n\nFig 1 is not quite readable. The figure further makes POMONAG look like three single-objective tasks combined rather than a four-objective task.\n* Improve readability, especially on the right-hand side.\n* Better illustrate the integration of all four objectives in a unified multi-objective framework if these objectives are not just simply added together (*see the first part of my comments*).\n * Provide a clearer visual representation of how POMONAG handles the trade-offs between objectives (*see the first part of my comments*).\n--- \nLine 186, the term noisy architecture is not explained. \n* Provide a brief explanation of what \"noisy architecture\" means in this context and how it relates to the diffusion process in DiffusionNAG.\n--- \nEquations and their connection to the processes/algorithms are not numbered and not clearly explained. \n* Number all equations for easy reference\n* Clearly label the equation at Line 183. Is this equation for the Reverse Diffusion Process? Clarify that connection.\n* Provide a brief explanation of the symbols used in this equation and other key equations.\n* Explain the purpose of transformation s_θ(A_t,t).\n* Explain the exact differences between the Reverse Diffusion Process and the Reverse Diffusion Guidance Process.\n--- \nLine 280, \"Four are dedicated to the respective estimation of accuracy, parameters, MACs, and inference latency of noisy architectures during the diffusion phase. \" \n* Explain why not use these four metrics for denoised architectures as well.\n* Justify the point that the denoised architecture uses accuracy as its only metric.\n--- \n\nExplain the reason why POMONAG utilises Vision Transformer ViT-B-16 instead of other models (Line 286).\n\n--- \nIt is good to see the Spearman correlation experiment. That is very important in NAS studies. However, for a thorough comparison of correlation, it should be done on a set of tasks like NAS-Bench-Suite-Zero (Krishnakumar et al. NeurIPS'22).\n* Perform a similar thorough comparison comparing correlations on different tasks using different search spaces.\n--- \nIn lines 400-402, the same latex problem appeared several times, ` not ' for the left quotation marks, Accuracy, Params, MACS ... \\\n* Fix these formatting issues.\n--- \nValidity, uniqueness and novelty are nice metrics for a population of solutions but not so critical for tasks that focus on accuracy and speed. What is the point of being excellent on these points but without good accuracy and speed?\n* Explain the significance of these three additional metrics: validity, uniqueness and novelty.\n* Show example how these measures can help improve the quality of generated architectures in POMONAG." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I have several questions about this work.\n\n1, How to decide the scaling factors? Since the intervals are [1000,5000], [100,500], [100,500], [100,500], and the values seesm to be integer, then the whole factor space equals 4000 * 400 * 400 * 400, which is quite huge. And the authors present one setting for NASBench201 and other experiments, respectively, so I am wondering whether there is some method or strategy to choose such factors? \n\n2, This work extends the basic motivation of DiffusionNAG, which is rather good and natural. Such extension include three more factors, including number of parameters, number of MACs, and the inference latency. But I am curious that, how about the performance of POMONAG if just considering adding one factor? \n\n3, From one factor, say, accuracy, to three more factors seems strenghening the proposed POMONAG, but my question is, the working mechanism of DiffusionNAG and POMONAG the same different? Although the two diffusion processes consider different factors, which is the obvious difference, but the analysis or discussion is important to interpret this issue." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This paper introduces the ParetoOptimal Many-Objective Neural Architecture Generator (POMONAG), extending DiffusionNAG through a many-objective diffusion process. POMONAG simultaneously considers accuracy, the number of parameters, multiply-accumulate operations (MACs), and inference latency. The experiments validate the performance of the proposed model." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper is a direct extension based on DiffusionNAG, which can deal with multi-objective optimization in NAS. These objectives include accuracy, the number of parameters, multiply-accumulate operations (MACs), and inference latency. This motivation is good and natural, and the authors expressed their work clearly, from the motivation to the experiments results. Some details need to be clarified." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1, The multi-objective optimization problem formulation in this work can be given first, which then can be solved by the proposed weighted factors in the reverse diffusion process. But maybe the authors can consider other ways to sovle this. For example, using four single reverse diffusion process each targeting one factor, as DiffusionNAG did, then using multi-objective optimization for further trade-off may also work well.\n\n2, The theoretical analysis should be strehghen. One objective to many objective is a breakthrough, but such process needs more analysis or discussion. Current work lacks such in-depth thinking. \n\n3, Several predictors are needed in this work, but the detailed information these predictors are missing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see the weaknesses. If the concerns raised are well addressed, I am glad to increase my rating." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1) The idea the overall framework of the proposed POMONAG method is simple and easy to understand. \n2) The details of the method and experiments are clearly stated. \n3) Generating neural architectures in the multi-objective manner is an important research topic." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents the POMONAG method to generate neural architectures in the multi-objective manner. Specifically, the overall framework of POMONAG is designed based on that of DiffusionNAG, in order to achieve better performance in terms of number of parameters, MACs, and inference latency beyond the accuracy. There are four key parts designed to achieve this goal, i.e., the many-objective reverse diffusion guidance, the meta-dataset, the score network and performance predictors, and the pareto front filtering and stretching. The experimental results in NAS-Bench-201 and MobileNetV3 search spaces demonstrates the effectiveness of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) My major concern is about the motivation of this work. Specifically, there are four objectives considered, i.e., the accuracy, the number of parameters, MACs, and the inference latency. However, the last three objectives do not demonstrate conflict relationship. For instance, the smaller number of parameters seems certain to lead to lower inference latency. In this case, the necessity for adopting multi-objective optimization is limited. \n2) The novelty of the proposed method needs further discussion. Specifically, the proposed method seems to build on DiffusionNAG with the cooperation of the multi-objective optimization. It seems that the POMONAG is just a simple combination of these methods. More discussions in terms of the seminal contribution of POMONAG is needed. \n3) How the hyperparameters $k_{\\phi}$, $k_{\\pi}$, $k_{\\mu}$, and $k_{\\lambda}$ determined? It is suggested to provided more details in terms of the hyper-parameter study for these hyperparameters. \n4) The search cost of POMONAG is not well presented. In the pipeline of POMONAG, I think the pre-training process, the training of the score network, and the training for the performance predictors will introduce much additional search cost beyond the architecture generation. However, I cannot find any details about the overall search cost and the search cost for the above components. \n5) I am curious about why only one trained architecture is enough for POMONAG? Maybe more discussions or analysis are helpful to give more insights for this point. \n6) Lack of experimental results on more challenging tasks (i.e., the classification accuracy on ImageNet-1K). More results on such datasets are helpful to enhance the experiments." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "How did you choose the search spaces to apply POMONAG? \nThe algorithmic contribution seems like a limited extension of DiffusionNAG. What complication arose from integrating multi-objective NAS into DIffusionNAG?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Strong writing, ideas are explained well and thorough\nThe experiments are presented well and results are thorough\nNovelty is presented in 2 algorithmic improvements and the contribution of a multi-objective meta dataset" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The work presents an extension to DiffusionNAG and incorporates multi-objective search. Model complexity, computational efficiency, and inference latency are key measures captured through number of parameters, MACs, and latency estimation. These measures are recorded in a meta dataset for NASBench201 and MobileNetV3 with 10k and 20k architectures respectively. During search, pareto front filtering segments three regions corresponding to high accuracy, high efficiency, and best balance of the two using the auxiliary metrics from earlier. The experimental results are promising across a sufficiently diverse set of benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "For transferable NAS, the choice of benchmarks are interesting, TransNASBench provides a NAS dataset specifically for transferability in NAS. Exploring performance on this dataset would have been nice\nMobileNetV3 and NB201 are also fairly dated search spaces, performance in more recent search space or architecture styles (vit) should be explored\nThe specific details of the algorithmic contribution are a bit vague. How is pareto front filtering done? \nImageNet results are sparse and comparison to modern NAS methods on this benchmark are sparse" }, "withdrawal_confirmation": null }, { "TLDR": { "value": "POMONAG is a dataset-aware Transferable Neural Architecture Search technique for Pareto-Optimal Many-Ojective generation of state-of-the-art efficient neural architectures." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024pomonag,\ntitle={{POMONAG}: Pareto-Optimal Many-Objective Neural Architecture Generator},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4y4t7yOvJO},\nnote={under review}\n}" }, "abstract": { "value": "Neural Architecture Search (NAS) automates the design of neural network architectures, minimising dependence on human expertise and iterative experimentation. While NAS methods are often computationally intensive and dataset-specific, employing auxiliary predictors to estimate architecture properties has proven extremely beneficial. These predictors substantially reduce the number of models requiring training, thereby decreasing overall search time. This strategy is frequently utilised to generate architectures satisfying multiple computational constraints.\nRecently, Transferable Neural Architecture Search (Transferable NAS) has emerged, generalising the search process from being dataset-dependent to task-dependent. In this domain, DiffusionNAG stands as a state-of-the-art method. This diffusion-based method streamlines computation, generating architectures optimised for accuracy on unseen datasets without the need for further adaptation. However, by concentrating exclusively on accuracy, DiffusionNAG neglects other crucial objectives like model complexity, computational efficiency, and inference latency -- factors essential for deploying models in resource-constrained, real-world environments.\nThis paper introduces the Pareto-Optimal Many-Objective Neural Architecture Generator (POMONAG), extending DiffusionNAG through a many-objective diffusion process. POMONAG simultaneously considers accuracy, the number of parameters, multiply-accumulate operations (MACs), and inference latency. It integrates Performance Predictor models to estimate these secondary metrics and guide the diffusion gradients. POMONAG's optimisation is enhanced by expanding its training Meta-Dataset, applying Pareto Front Filtering to generated architectures, and refining embeddings for conditional generation. These enhancements enable POMONAG to generate Pareto-optimal architectures that outperform the previous state-of-the-art in both performance and efficiency.\nResults were validated on two distinct search spaces -- NASBench201 and MobileNetV3 -- and evaluated across 15 image classification datasets." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Neural Architecture Search", "Many-Objective", "Pareto-Optimal", "Meta-Dataset", "Transferable Neural Architecture Search" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/8ce7489c940467a310a9d0b8e1f97a8a1a529b97.pdf" }, "presentation": null, "primary_area": { "value": "transfer learning, meta learning, and lifelong learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Line 288: `... of each sample in the entire medical corpus.' what does each sample indicate (documents, QA pairs or anything else?)? Are the samples drawn from the only the dataset being evaluated or all of them combined?\n\n- How was the 50B domain text obtained from wiki-medical-terms? The website seems to indicate that the corpus has 6k medical terms and their descriptions. Does the whole terms + description have 50B tokens? Any other relevant statistics?\n- The paper's main contribution seems to arise due to creating the High Quality (HQ) partition using KenLM- Could the authors add more information about how this was performed? For e.g., what were size of _n_ if an n-gram-based approach was used?\n- Creating HQ partition could have been done in other ways- entropy, ranking or using MLE for importance. Can the authors comment why KenLM was chosen. Can they compare this selection with others? Do they work in similar ways/show similar performance?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is reasonably well organized and written. \n- The findings are well explained and justified with empirical analyses where required.\n- The authors conduct extensive experimentation to cover different possible research questions\n\n- The concept of stability gap is not new and has been extensively studied in Computer vision but relatively less in NLP. The paper draws it's research question from this and possible solutions from CV. The paper compares its proposed strategies with existing work, e.g. Ibrahim et al (Rewarm and decay), Replay (Chen et al) etc." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The manuscript focuses on the problem of stability gap- i.e., LLMs dropping their performance drastically when continually pretrained on a new domain and then recovering performance gradually. The manuscript demonstrates stability gap using the medical domain using relatively smaller language model and proposes (three) strategies to overcome and stabilize pre-training loss- (1) continual pre-training with a random partition of the domain across multiple epochs (2) continual pre-training using a notion of high-quality tokens selected using KenLM (3) Utilizing existing pre-training data-mixture ratios to selectively replace the current corpora with target domain corpora. The manuscript then applies the strategy to Llama-3-8B- in continually pretrain and fine-tuning settings." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The paper uses DEITA and KenLM for assessing the quality of samples in the target domain.\n\n Need a baseline with only Continually pretrained with all data (all data vs only 50B) vs proposed strategy\n- Table -1: The\tperformance vs 10B replay is pretty close. The performance difference seems to solely arise due to MedMCQA;\n may need statistical significance tests to see if the differences are due to proposed strategies or due to randomness.\n- Table 2: >20% performance jump again on MedMCQA for Physician vs LLaMa-3-8B Fine-tuned seems odd. Are there any possible explanations, especially the difference in performance for other datasets <5%. (Please add statistical significance tests- see last bullet)\n-\tPerformance could be possibly validated using statistical significance tests- either using permutation or signed rank tests. see- https://aclanthology.org/D12-1091/" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "* Why is the performance substantially better for your strategy on MedMCQA? The tasks, performance gains seem more mixed and not necessarily as beneficial. What about MedMCQA benchmark makes it benefit the most from the continual pre-training?\n* Does this technique work for other datasets? In looking at the legal dataset results in Appendix F, there are similar findings suggested for the zero-shot but the experimental comparisons." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* The paper derives insights from stability gaps introduced in the context of visual models for continual learning to explain the behavior of performance drops with LLM continual pre-training for the specialized domain.\n* Evaluation results with various biomedical-domain fine-tuned LLMs and QA datasets demonstrate the potential of the strategy.\n* For some tasks and datasets, there is a noticeable improvement using less number of training tokens, especially on the MedMCQA task." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper suggests that performance instability when training LLMs for specialized domains arises from distribution shifts. As such, they propose a new continual pre-training strategy that incorporates data quality and corpus distribution to identify \"better\" samples. In addition, the idea is to use these better subsets of samples and train for more epochs to ensure the LLM is in the performance recovery phase. The authors illustrate their performance on 4 benchmark QA datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "* The base architecture used is only the OpenLlama3B model with a single parameter size. The natural question is whether such a strategy is applicable across various LLM families and sizes (for example, GPT-NeoX was used by Ibrahim et al. with a 10B parameter model which might be comparable to the 8B rather than the 70B). Can you provide a comparison against GPT-NeoX 10B to provide a meaningful evaluation of your strategies?\n* The motivation for the learning strategy is under a fixed computational budget, which seems to be only related to the number of training tokens and not the number of epochs. Can you explicitly define computational budget and then evaluate a scenario where token count and epoch count are kept constant to better understand the tradeoffs when considering a computational budget? This is a more elaborate setting than Section 3 which only assessed 5 epochs. \n* The methodology, efficient learning strategies, and evaluation sections, all seemingly blend together without necessarily a coherent story or separation of sections. For example, in section 3, the differences between the two subsections seem to blend together whereas it would have been better to introduce the stability gap and demonstrate that the instability that is often observed seemingly is explained in the context of this, and should be done for one common set of experiments (note that there is swapping between medical domain, common sense task performance but with very little context for these experiments until Section 5). Section 4 seems to be more of an ablation study rolled in with their own method. As such, while it seems like the authors have done a lot of reasonable experiments, untangling what they are introducing and evaluating is very hard to understand without multiple reads. My suggestion would be to reorganize so that both section 3 and 4 are one contiguous section, where the first subsection focuses on motivating the stability gap in the context and then providing the strategy to mitigate this by choosing higher-quality samples. Section 5 can then focus on experiments where they are concisely targeting specific aspects of the strategy.\n* There are a lot of results, but limited discussion about them, especially comparison of performance. Please provide a more detailed discussion of your results. Moreover, it would be helpful to clarify which fine-tuned models may not be tuned on the same task so the performance might be hindered by this, whereas others might be fine-tuned on the task so it might be reasonable to expect them to do well. To accommodate this expansion, space can be made by shrinking some of the figures.\n * Some of the graphs do not provide sufficiently more information. For example Figure 2 (b) reports the beginning of only 1 model for the millions of tokens, but the trend doesn't seem to be that much more informative than Figure 2a. Similarly, much of the motivation was for specialized domain but there is only a focus on medical domain whereas it would have been more compelling with Appendix B results embedded here." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. I am not clear that how the high quality is obtained from original medical corpus.Can you further explain the quality evaluation metric for the data selection? \n2. The figure 4 (a) is not clear to me. What's the x-axis represent? Can you further explain this Figure 4(a) and your finding? \n3. The mixture strategy confused me. Can you further explain the mixture strategies? Specifically, \n\"we follow the Llama mixture rate (Touvron et al., 2023a) to collect 5 billion tokens initially. We then replace the CC and C4 data (82% of the 5 billion tokens) with medical tokens sampled from the highest quality 5 billion medical tokens (HQ-5b). \" \nWhat's the initial 5 billion tokens ? How you further replace the token. \n4. Is stability gap existing on larger models? like 13B or larger models? Could you further conduct experiments on larger model to show the importance of the proposed issue?\n5. Strategy 1 trains more epochs on smaller dataset may have higher chance to overfit. Can you further compare the continual training's performance on other OOD benchmark to show the overfitting issue (e.g. DROP, GSM8K, HumanEval etc).\n6. Does the ' stability gap' changed by using different learning rate and warm-up strategies?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "1. The paper's motivation is clear, with well-structured on problems in continue pertaining, proposed strategies, and results. \n2. Authors conducts experiments on different benchmarks across medical and law, to show the effectiveness of proposed methods in the continue-pretraining." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the LLM behaviors when adapting to specific domains via continual pre-training. Authors point out unexpected \"stability gap\", which is an initial drop in performance before subsequent recovery. Authors provides three training strategies on this unexpected trend and conduct experiments on medical and law benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The proposed strategies seems similar with existing works conclusions. For examples, high quality data is important for model training [1,2] , using similar data mixture rate to the pre-training data to alleviate data distribution shift [3,4].\n2. The experiments only conduct on relatively small models. The gap may be due to the the small model is not robust enough on the new dataset. It is. unsure that if the larger models ( for example, 13B, 70B ) meet the same issue on continue pertaining.\n3. The IFT model comparison is unfair to me due to some IFT models do not tuned on specific training dataset and they have different base models. \n4. It is unsure that if the proposed IFT models is overfitting into the evaluation dataset by building IFT dataset based on original training data.\n\n\n[1] Chen at al (2023). AlpaGasus: Training a Better Alpaca with Fewer Data\n[2] Zhou et al (2023). LIMA: Less Is More for Alignment\n[3] Parmar et al (2024). Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models.\n[4] Ibrahim et al (2024). Simple and Scalable Strategies to Continually Pre-train Large Language Models" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "1) The stability gap concept proposed by previous studies is about the inability to maintain performance on **prior** tasks and the one mentioned in this paper is about the performance in the **new** target task. How are they two related in your experiments?\n2) The initial drop in the averaged accuracy of the LLM on the medical tasks looks very insignificant.\n\t- Have you done a statistical test to verify this? \n\t- Is the small drop (<1%) in line with the findings of previous stability gap studies?\n3) Data Mixture Results (Figure 4b and 4c):\n\t- The authors may need to compare the proposed strategies with the baseline (full data with multiple epochs).\n\t- The average medical and commonsense performance seems to drop in the 5th epoch. Why is that the case? What would happen if you continue the pretraining to 6th, 7th, ... epoch?\n4) How similar is the \"high-quality\" medical reference corpus to the downstream tasks?\n\t- If you run the KenLM model on the downstream datasets, what is the perplexity? Would the perplexity be very low too?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The proposed strategies are easy to implement.\n- The LLM fine-tuned with the proposed strategies achieved the highest averaged accuracy score on a suite of medical question answering tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper uses the concept of stability gap to explain the initial drop in LLM performance during continual pretraining in a new domain. The authors propose three training strategies to address the initial instability: 1) continually pretrain the LLM on a properly sized corpus subset for multiple epochs; 2) Continually pretrain LLM on a high-quality corpus subset; 3) Using data mixture rate that is similar to the pretraining data. The proposed strategies improve the accuracy of the LLM in the new domain when compared to the existing continual pretraining techniques." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "## Major\n- It is important to justify the methods of Muennighoff et al. (2024) and Lin et al. (2024) used in this paper. (I assume the four subsequent sentences explain the method (L158-161)). Here are some missing details:\n - Why was KenLM chosen?\n - What is a \"high-quality medical reference corpus\"? how do you define it? This is a fairly critical point because the \"highest-quality\" medical corpus can also be defined as those that resemble the downstream tasks the most, which makes the findings more expected (the closer the continual pretraining data is to the downstream tasks, the better the model will perform in the downstream tasks).\n- The authors claim that the average accuracy of the LLM on the medical tasks initially drops and rises during the continual pretraining.\n\t- However, the drop itself does not look significant (less than 1% averaged accuracy). This makes the observation less strong. (See Question 2)\n- This paper contains a flawed assumption due to the lack of access to the pretraining corpus. If a stability gap was proposed to explain the ability of the model to maintain performance on previous tasks, such an analysis cannot be achieved if we do not have access to the pretraining corpus.\n\t- The authors claimed (L233-235) that language modelling loss also preserves general knowledge and text modelling capabilities, which is a big assumption that is not backed by any evidence.\n\t- Note that text modelling capabilities may still be preserved via language modelling loss during the domain adaptation (continual) pretraining, however, we cannot guarantee that the general knowledge is still being preserved.\n\t- Additionally, there is no guarantee that the continual pretraining corpus was not included in the pretraining corpus. To examine this, the authors may have to conduct a pretraining from scratch.\n- There exists a logical gap between the concepts of relative weight update, stability gradient, and instruction-following ability.\n\t- The authors concluded that the relative weight update indicates the stability gradient and, in turn, instruction-following ability (L241-253). However, there is no guarantee that relative weight update relates to stability gradient, let alone instruction-following capability.\n - Additional experiments using pretraining from scratch may help understand this phenomenon better.\n- There are several mentions of a \"properly sized\" subset. However, they are not properly defined.\n- The performance improvement (Figure 4) when compared to the baseline seems to be <1%. This does not look very significant.\n\n## Minor\n- Note that the submission and paper titles are different\n- Abstract is generally filled with jargon which makes it harder to follow.\n- L50-51: The last sentence of paragraph 1 in the Introduction can benefit from some citations.\n- L56: Missing citation for \"Previous research\"\nThe introduction section still contains a lot of undefined jargon (i.e., \"proper size\", \"highest-quality tokens\", \"data mixture\")\n- L194: Concluding that the \"LLM has acquired medical domain knowledge\" based on the perplexity score is a bit of an overclaim. Consider rephrasing it.\n- Table 2: This misses the performance of the Llama-3-8B models without fine-tuning.\n- The authors claim that the proposed strategies are computationally more efficient. By how much exactly? What metrics should you evaluate this on?\n\n## Very minor (e.g., typos, etc)\n- Use consistent verb tense (many inconsistent uses of present and past tenses)\n- Typo L15: \"phrase\" -> \"phase\"\n- L68: Instead of \"harness\" perhaps \"mitigate\" it? since you would like to mitigate the stability gap as opposed to harnessing it.\n- Typo L125: lowercase \"Language models\"\n- Typo L125: \"RoBERTa\"\n- Page 4: Perhaps observations 1 and 2 can be swapped because in practice we may not know the downstream tasks during the (continual) pretraining phase.\n- Figure 6b: The caption does not seem to be correct. The figure seems to show accuracy during law continual pretraining, while the caption is about relative parameter updates during the medical continual pretraining process." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024towards,\ntitle={Towards Efficient and No Forgetting Domain Continual Pretraining by Mitigating the Stability Gap},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4y6Q98hJzr},\nnote={under review}\n}" }, "abstract": { "value": "Adapting Large Language Models (LLMs) to specialized domains like medicine and law through domain continual pre-training has become the cutting-edge method. However, contrary to our expectations of immediate gains, we’ve uncovered a surprising phenomenon: a temporary performance drop at the start of the process, followed by a performance recovery phrase. This drop is not only unexpected but remarkably consistent across different model sizes and domains, such as medical and law. To gain a deeper understanding of this issue, we introduce the concept of stability gap—borrowed from visual models dealing with new class classifications—to explain this initial drop in LLM performance. Based on this concept, we hypothesize that the initial performance drop arises from instability in the model’s general abilities, which we further validated through our experiments.\nWe further reveal that this initial instability is intricately tied to training settings that involve distribution shifts.\nTo address this initial instability and enhance LLM performance within a fixed compute budget, we propose one training strategy that reduces the instability by increasing the epoch number, along with two data sampling strategies focused on data quality and corpus distribution.\nWe conduct various experiments on Llama-family models to validate the effectiveness of our strategies in both medical and legal continual pre-training and instruction tuning. For example, our strategies improve the average medical task performance of the OpenLlama-3B model from 36.2\\% to 40.7\\% with only 40\\% of the original training budget and enhance the average general task performance without causing forgetting. \nFurthermore, we apply our strategies to continually pre-train and instruction-tune the Llama-3-8B model. The resulting model, Llama-3-Physician, achieves the best medical performance among current open-source models and performs comparably to or even better than GPT-4 on several medical benchmarks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Continual pretraining" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/ac9215997c666de4c1a5b41a9b1c0a334f57d141.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Towards Efficient and No Forgetting Domain Continual Pretraining by Mitigating the Stability Gap" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": { "value": "Thank you to all the reviewers for their time and valuable feedback. The original intent of our method was to replace the basic LSTM or GRU modules in various complex models to accelerate training and inference speed. There are aspects of the paper that need improvement, and after further discussion, we have decided to withdraw the manuscript for further refinement. Once again, we sincerely appreciate the reviewers' efforts." }, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": { "value": "I have read and agree with the venue's withdrawal policy on behalf of myself and my co-authors." } }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See weaknesses." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "- Easy to follow and clearly written;\n- Lightweight solution for text classification compared to transformer models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces an RNN-based model called Fast Salient Factor Concentration (FSFC), designed to improve training efficiency in text classification by using short-term memory and semantic clustering. While FSFC's concept is intuitive, it has several critical limitations. The approach offers limited innovation, appearing incremental relative to existing RNN and attention-based techniques. Additionally, the research feels outdated, as NLP has largely moved toward transformer-based models. Performance evaluations yield mixed results: FSFC reduces training time but sacrifices accuracy on some datasets. Moreover, baseline comparisons are insufficient, with FSFC only benchmarked against LSTM and GRU models, excluding state-of-the-art transformers like RoBERTa and recent LLMs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Limited novelty: The concept of text compression and segmentation is well-explored, with similar techniques in attention mechanisms and memory simplification.\n- Outdated approach: It’s advisable for the authors to employ BERT-based models or LLMs, as transformers are now the NLP standard. Although addressing transformer complexity with RNNs is a good direction, applying it to basic tasks like text classification feels outdated." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "1. Does your model reduce the inference time?" }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "This paper proposes a structure that is 5 times faster to train than LSTM." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper provides a method to remove the gating mechanisms of LSTM and condense memory. The proposed method achieves better training speed while retaining accuracy." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. It is tough to understand Figure 1. I can not find any RNN or recurrent in this figure. What is your whole network? Moreover, this figure doesn't have detailed captions.\n\n2. Why does long-term memory not matter in classification? Please provide justifications with citations or experiments.\n\n3. This paper does not compare with other improvements on LSTM. Do other improvements over LSTM reduce the training time and achieve better accuracy?\n\n4. This paper does not provide details about the experiment setup. Although we do not know what the network structure proposed in this paper looks like, nor do we know how the configuration compares to GRU and LSTM, or the length of the experimental data.\n\n5. E-score is not more effective than just presenting accuracy and time." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. What is the motivation to propose an alternative to RNN for text classification? What problems are you trying to solve? Does \"the long-term memory mechanisms of traditional RNNs do not fully align with human cognitive learning processes\" really matter? How does it impact the performance in text classification tasks? Could you give us some concrete examples of how the misalignment between RNNs and human cognitive processes impacts performance in text classification tasks?\n\n2. What is the contribution of FSFC? The author should discuss how FSFC compares to or complements more recent approaches in NLP. RNN-based methods for text classification are quite outdated now. While I am not against RNNs, achieving minor improvements and reducing training time seems to bring little new knowledge to the current NLP community.\n\n3. How about employing other methods like BERT and LLMs? What is the value of an alternative to RNN compared with pretrained language models? Why do we still need RNN-based methods for text classification?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper is well-organized, and the writing is fine.\n\n2. The paper evaluates the performance of FSFC on four datasets. The experimental results show the effectiveness and efficiency of FSFC." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes Fast Salient Factor Concentration (FSFC) RNN, a new architecture for classification tasks, to enhance the processing of crucial information by dynamically clustering and compressing semantic information. The performance on YelpReviewFull proves that FSFC has a higher accuracy with less training time." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The motivation of this paper is not very clear. I'm not sure what problems this paper is trying to solve (Text classification? An alternative to RNN? An alternative to RNN on Text classification?). It would be beneficial to specify what problems the authors aim to address—are they focusing on text classification, proposing an alternative to RNNs, or something else? Anyway, the contribution seems limited.\n\n2. The idea presented in this paper does not appear particularly interesting. It proposes an alternative to RNNs for text classification, but I believe it lacks significant contributions to the current NLP community. Nowadays, many practitioners favor pre-trained models over RNNs for text classification tasks. Additionally, large language models (LLMs) tend to focus on multi-tasking rather than on single NLP tasks.\n\n3. If the goal is to propose a new RNN, conducting experiments only on text classification is insufficient to verify the method's generalization.\n\n4. Even for text classification tasks, the models compared in the paper are not comprehensive (e.g., ELMO, BERT, LLMs, and so on). The paper lacks comparisons with these strong methods for text classification. As I mentioned in Weakness 2, these models are precisely the kinds of models that are commonly utilized in the field of text classification today. If the paper focuses on text classification, not comparing with these mainstream models would be unfair." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@misc{\nxia2024fast,\ntitle={Fast Salient Factor Concentration ({FSFC}) Recurrent Neural Network for Text Classification},\nauthor={Weihao Xia and Huachuan Wang and Qiu Chen and Junlong Ma and James Ting-Ho Lo},\nyear={2024},\nurl={https://openreview.net/forum?id=4ymHtDAlBv}\n}" }, "abstract": { "value": "Models based on Recurrent Neural Networks (RNNs) have been widely employed for text classification tasks. Traditional RNNs primarily emphasize long-term memory capabilities. However, this approach does not fully align with human cognitive learning processes, particularly in the context of classification tasks. The human brain typically extracts essential information relevant to the classification categories, disregards irrelevant details, and compresses the input to accelerate decision-making. Inspired by this, we propose a novel architecture, the Fast Salient Factor Concentration (FSFC) RNN, specifically designed for classification tasks. FSFC dynamically clusters and compresses semantic information by leveraging the short-term memory capabilities of recurrent neural networks. Experimental results demonstrate that FSFC achieves performance comparable to existing RNNs, while significantly improving training efficiency in classification tasks. Based on the YelpReviewFull dataset, FSFC improves accuracy by 1.37% over Long Short-Term Memory (LSTM), while reducing training time by 86%. Additionally, we propose a new evaluation metric, E-score, which integrates both accuracy and time efficiency to comprehensively assess the overall performance of each network." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": { "value": [ "~Weihao_Xia2", "~Huachuan_Wang3", "~Qiu_Chen1", "~Junlong_Ma3", "~James_Ting-Ho_Lo1" ] }, "authors": { "value": [ "Weihao Xia", "Huachuan Wang", "Qiu Chen", "Junlong Ma", "James Ting-Ho Lo" ] }, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Text Classification", "Semantic Information Clustering", "Recurrent Neural Network" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": { "value": "xia|fast_salient_factor_concentration_fsfc_recurrent_neural_network_for_text_classification" }, "pdf": { "value": "/pdf/6c754b36fb662482c6e84cee434cb837a190bbed.pdf" }, "presentation": null, "primary_area": { "value": "learning on time series and dynamical systems" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Fast Salient Factor Concentration (FSFC) Recurrent Neural Network for Text Classification" }, "venue": { "value": "ICLR 2025 Conference Withdrawn Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Withdrawn_Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. What is the motivation and justification for the necessity of the design that replaces half of the third stage and full last stage conv blocks with transformer blocks? Please refer to Weaknesses 1. \n\n2. I wonder what is the performance and latency of MHA as the missing step between 'kernel sz.' and 'SHA' in Figure 2." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This paper is well-organized and easy to follow. Detailed design specifications and comprehensive experiments enhanced the integrity of the article and demonstrated its contributions.\n\nThe main contribution, SHMA, provides a new approach to designing efficient attention and Transformer blocks. The resulting iFormer series outperforms sota baseline mobile networks with stronger performance and lower latency." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper designs iFormer, a new family of efficient mobile vision networks combining ConvNet and Transformers. The iFormer evolves from ConvNeXt with a series of efficiency designs. \n\nSingle-Head Modulated Attention(SHMA) is proposed as substitutional Transformer blocks to replace part of the Conv blocks in later stages of the enhanced ConvNeXt. SHMA replaces multi-head attention with single-head attention to improve efficiency and introduces a modulation mechanism to boost performance. \n\nThe resulting iFormer series achieves the best performance compared with state-of-the-art mobile-level models on different downstream tasks with lower latency." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "W1: \n\nThe motivation and necessity of substituting half of the conv blocks at the third stage and all blocks at the last stage into Transformer blocks in ConvNeXt are still not very clear. From Figure 2, changing the conv blocks into SHA blocks gains a 0.4% improvement in performance but is also 0.12 ms (about 10%) slower. I'd like to know further explanation for this design and ablation studies on the choice of stages or different ratios of Conv versus Transformer blocks if possible. \n\n\nW2:\n\nAccording to the citation of SHViT in this paper, I suppose the SHA refers to the Single-head self-Attention in SHViT design. But in Figure 4, full channels of input (CxHxW) are projected to Q/K/V (CxL) which does not align with the design of SHA in SHViT but looks like the traditional definition of Single-head attention that performs a self-attention on all channels of input using a single head. \n\nConsidering there are limited words about the details of SHA in this paper, I would expect further specification of which SHA is used in iFormer and comply with the pipeline figure accordingly. \n\nW3:\n\nIn this paper, the additional reshaping operations in MHA are considered as the reason for the slower inference speed compared with SHA. But multiple factors have an impact on the runtime speed difference and there's no evidence to support the extra runtime only or mainly comes from extra reshapings. \n\nFirst, depending on the code implementation, replacing MHA with Single-head self-Attention may remove the reshaping operation in self-attention, but also introduce additional split and concat operations. And generally, split and concat operations cost more memory and are slower than reshape. \n\nSecondly, SHA applies self-attention on fewer channels, which largely reduces the computational cost and speeds up runtime. \n\nTherefore I suggest the authors conduct an ablation study or provide empirical evidence to isolate the impact of reshaping operations versus other factors like split/concat operation and reduced self-attention channels on the inference speed. This would help clarify the main factors contributing to SHA's efficiency and provide a more comprehensive understanding of the proposed method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "please refer to weakness" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The study of model architecture could inspire further exploration in designing more efficient architectures.\n2. The paper is well-organized and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new family of mobile hybrid vision networks. By integrating the rapid local representation capability of convolution with the efficient global modeling ability of self-attention, the proposed architecture, iFormer, achieves significant performance in classification and several downstream tasks, while maintaining low latency on mobile devices for high-resolution inputs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. In Table 1, iFormer-S achieves the same latency as RepViT-M1.0 with slightly fewer parameters, yet in larger variants, iFormer achieves lower latency with substantially more parameters compared to RepViT. What is the reason for this difference?\n2. Some studies are not included in the comparison or the related wotk section, such as [1, 2].\n\n[1] Cmt: Convolutional neural networks meet vision transformers.\n\n[2] Learning efficient vision transformers via fine-grained manifold distillation." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "The largest model shown by iFormer, iFormer-L, is only about 15M, which isn’t considered large, even for edge devices, especially since recent edge LLMs can reach 1B parameters. I wonder how well a larger iFormer (around 100M) would perform." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "I think the logic of exploration in this article, starting with ConvNeXt, first “lightening” the ConvNeXt to create a streamlined\nlightweight network, then exploring the attention module, is reasonable. \n\nI think the analysis about “cosine similarity between multiples” proves that using a single attention is good and worth supporting. \n\nI think the experiment reported in this paper is comprehensive (imagenet, coco, ade-20k). The paper also reports some knowledge distillation results, which is suitable in mobile network papers." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a mobile hybrid vision network, iFormer. The paper goes from ConvNeXt to a lightweight mobile network. iFormer removes memory-intensive operations in MHA and employs an efficient modulation mechanism. The author conduct standard benchmark experiments on ImageNet, COCO and ADE20K." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1: \nSingle head self-attention has been conducted in \"Shvit: Single-head vision transformer with memory efficient macro design\" .\n\nAlternative to standard self-attention has been conducted in GhostNetV2.\n\nModulation in the token mixer module has been conducted in Conv2Former. \n\nThis paper references many related methods, and while that is one approach, I don't think it stands out. Although such research is a decent format, I believe it impacts the novelty of this paper. \n\n2: \nThe process of evolving from the ConvNeXt baseline to the lightweight iFormer may not apply to slightly larger models, and some steps show very minimal improvements, making them hard to justify." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see the weaknesses." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper is easy to follow, with clear writing and presentation.\n2. Evaluation results are comprehensive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a new family of mobile hybrid vision networks, called iFormer, by integrating the fast local representation capacity of convolution with the efficient global modeling ability of self-attention." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. How does this method compare with neural architecture search (NAS) methods?\n\n2. How does the designed model perform on other mobile devices, such as NVIDIA Jetson Nano or Raspberry Pi?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See weakness" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. fast local representation capacity of convolution and the efficient global modeling proficiency of the proposed SHMA\n2. A series of novel techniques such as stack of overlapping convolution instead of aggressive non-overlapping patch in the early layers\n3. The model is structured in four stages. The early stages use fast convolution to capture local features efficiently, using a modified and lightweight version of ConvNeXt optimized for mobile latency.\n4. In the lower-resolution stages, self-attention is used to model long-range dependencies. To address the challenges of traditional multi-head self-attention (MHA), the authors propose SHMA, which uses a single-head attention mechanism to minimize memory costs while retaining high performance. SHMA reduces latency by optimizing reshaping operations and leveraging spatial context interactions. SHMA is combined with a parallel feature extraction branch to enhance feature representation. The outputs from both branches are fused to enable dynamic information exchange, mitigating any performance drop caused by simplifying MHA" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Summary\n\nThis paper proposes a mobile friendly vision network that improves the latency and accuracy by combining the strengths of both CNNs and ViTs. The novel aspect of this work is the single head modulation self-attention (SHMA). This SHMA learns spatial context through optimized self-attention. It takes ConvNext as the base model and improves it further with various techniques. The authors streamline the ConvNeXt architecture, making it suitable for real-time use on mobile devices, such as the iPhone 13, focusing on reducing latency rather than FLOPs or parameter count. The combined techniques lead to more than 80% top-1 accuracy with 1.1ms latency on iphone 13. Overall a great contribution to the research community." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. What is the runtime complexity of iFormer network?\n2. When running on iPhone (mobile device), what is the peak memory consumption? \n3. How long the iPhone charge will last if an iFormer based app is run on certain fps?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024iformer,\ntitle={{IFORMER}: {INTEGRATING} {CONVNET} {AND} {TRANSFORMER} {FOR} {MOBILE} {APPLICATION}},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4ytHislqDS},\nnote={under review}\n}" }, "abstract": { "value": "We present a new family of mobile hybrid vision networks, called iFormer, with a\nfocus on optimizing latency and accuracy on mobile applications. iFormer effectively\nintegrates the fast local representation capacity of convolution with the efficient\nglobal modeling ability of self-attention. The local interactions are derived\nfrom transforming a standard convolutional network, i.e., ConvNeXt, to design a\nmore lightweight mobile network. Our newly introduced mobile modulation attention\nremoves memory-intensive operations in MHA and employs an efficient\nmodulation mechanism to boost dynamic global representational capacity. We\nconduct comprehensive experiments demonstrating that iFormer outperforms existing\nlightweight networks across various tasks. Notably, iFormer achieves an\nimpressive Top-1 accuracy of 80.4% on ImageNet-1k with a latency of only 1.10\nms on an iPhone 13, surpassing the recently proposed MobileNetV4 under similar\nlatency constraints. Additionally, our method shows significant improvements in\ndownstream tasks, including COCO object detection, instance segmentation, and\nADE20k semantic segmentation, while still maintaining low latency on mobile\ndevices for high-resolution inputs in these scenarios. The source code and trained\nmodels will be available soon." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Lightweight Networks", "Efficient Networks", "Vision Transformers", "Classification" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/a96c4be844147c134d9a3705bc06802889378e13.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/8e73896496d32bd6c4a9d12e6e9a71691d14677c.pdf" }, "title": { "value": "IFORMER: INTEGRATING CONVNET AND TRANSFORMER FOR MOBILE APPLICATION" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Why do the evaluation for code similarity detection use cosine similarity (l. 321) when the objective (l. 212) uses the l2 distance?\n2. What is the underlying metric for the Pass@k in the decompilation evaluation? Exact match, or some more lenient equivalent? It seems wrong to use exact match when, for instance, variable names would be arbitrary.\n3. In Table 4, the second row is exactly the \"Nova-1B\" row of Table 2, but I was under the impression that \"Nova-1B\" was more than just \"DeepSeekCoder + Nova's attention\", in particular the additional training data, and CL objective. Are the numbers off, or the caption, or did I miss something?\n4. When creating the assembly datasets (Appendix A.1), why go all the way to compiling executables, then using `objdump` for disassembling, with the associated possibilities of failure, rather than dump the assembly in the first place with `gcc -S`?\n5. Do you have preliminary results, citations, or intuition behind the \"normalizing\" step of the assembly language performed in Fig. 6, in particular the addition of spaces? Is that necessary?\n\nMinor points:\n1. In the numerator on l. 265, is $f_j^q$ supposed to be $f^q$? or $f_j^p$ for which the substitution wouldn't apply?\n2. l. 300, how many samples do the GPT models perform, then, to be able to compute the Pass@10 in Table 2?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Originality\n--------------\n1. While hierarchical attention mechanisms are not new, the design of this one is innovative in that: it takes into account the specific format and constraints of assembly instructions, and it accommodates for using regular tokens in the same sequence (e.g., natural text instructions).\n2. The contrastive objective losses, as well, encode a priori knowledge of the underlying data: compilation stages preserve semantics, and optimization stages are sequential.\n\nQuality\n----------\nThe different contributions are overall sensible, and contribute to the performance of the model. Experiments are adequately designed, and support the conclusions of the paper. The additional experiments help understand the role of the different contributions, in particular their effect on how embeddings get clustered and the effect it can have on the final model's performance.\n\nClarity\n---------\n1. The paper includes most of the relevant information, either in the main text or appendix. Relevant literature is mentioned and cited.\n2. Figures and examples make it much easier to understand the logic, especially Fig. 3.\n\nSignificance\n-----------------\n1. This work shows a significant improvement on benchmarks, sustained across model sizes, and adaptable to other models. This is an advancement on an important, developing field of machine learning applications.\n2. Given that these improvements do not require any in-depth change (e.g., to the vocabulary) and are compatible with already pre-trained model make it easier to experiment with in different settings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents a way of training an LLM to improve its performance on tasks that require understanding of assembly code, in particular code decompilation, and assembly code similarity detection.\n\nThis is achieved by several contributions:\n1. A multi-way, parallel corpus of programs written in C, as well as the corresponding assembly produced by `gcc` with different levels of optimization (0 to 3), used for further training of pre-trained LLMs.\n2. A hierarchical attention mechanism, structured to summarize the content of each instruction into the representation of a single token. This mechanism is compatible with existing models.\n3. Two auxiliary contrastive loss objectives: a \"functionality\" one that minimizes the distance between representations of the same original code, while maximizing the distance between representations of different code pieces, and an \"optimization\" one encoding the fact that further levels of optimization should increase the distance between program representations.\n\nTwo variants (with 1B and 6B parameters respectively) of a model trained with these changes, and further fine-tuned for the task of interest, show a large improvement over state-of-the-art." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Quality\n----------\n1. One of the 3 motivating cases in the introduction, malware detection, is not evaluated or considered at all in the rest of the paper. I understand the scope of the paper needs to end somewhere, but it would have strengthened the paper to include experiments on such a dataset.\n2. Details are missing in how the authors are certain that test data sets (both for decompilation and for similarity detection) do not overlap with any of the training data, including the pre-training data of DeepSeek-Coder, even inadvertently.\n3. An additional ablation on $\\textrm{Nova}_{-CL}$ would have helped see if there are any non-linear interactions between HA and CL.\n\nClarity\n---------\nThe overall organization of the paper could be improved. Many times, a concept, method, or setting is used in context before being formally explained. For instance:\n1. If the \"Related Work\" section is positioned earlier, it would help introduce the baseline models (DeepSeekCoder, LLM4Decompile) that are used in the previous \"Results\" section, as well as attention mechanisms, including LongCoder's, also used earlier.\n2. When describing the new datasets, it should be clear much earlier that \"source code\" really means \"C code\" (in the caption of Table 1, for instance), \"assembly\" is X86 assembly (or maybe X86-64? that's not so clear), that only `gcc` is considered as a compiler, and whether each \"program\" actually means a full executable program, or if it includes functions as well.\n3. Similarly, the contrastive losses mention \"the embedding\" of a function, which is quite ambiguous in transformers, especially if the model family (encoder-decoder?) is not mentioned.\n4. There is also a lot of ambiguity in notation, or the semantics of different objects. For instance:\n * Do Table 1, and Appendix A.2, refer to the original \"AnghaBench\" and \"The-Stack\" datasets, or the new datasets constructed by the authors in Section 2.1? Maybe it would be better to name the new ones.\n * In Functionality CL, l. 208 says it \"optimizes Nova with the constraint\", but a constraint is not a loss or objective. l. 215, \"constraints can be trained\" do not really make sense. It's also not obvious how the loss defined at l. 220 actually implements (a relaxation of) these constraints. It's also not explained if the sum over $f_i \\in F$ is actually done over all the million embeddings in the corpus, or how it's implemented in practice.\n * K is introduced in Section 2.5, and then in 3.3., but we don't know what kinds of values will be used in practice. Also, Table 2 uses \"Pass@K\", but that's not the same K.\n * In captions of Fig. 4 (b) and (d), the tables are more \"designs\" than \"implementations\"\n * In Fig. 4 (b), the 1-4 indices are unfortunate as, for instance, $O0_3$ reads a lot like `-oO3`\n * The equations at l. 220 and l. 266 have a really similar form, but the use of indices $i$ and $j$ is swapped between the two, making it a bit harder\n\nSignificance\n-----------------\nThe results are somewhat limited by the use of a single assembly language, and a single compiler, but this is acknowledged and does not seem like a fundamental limitation.\n\nMinor points\n-----------------\nl. 461: \"cauclated\" -> \"calculated\"?\n\nIn the bibliography:\n- Vaswani et al. is actually from 2017, not 2023 (though the arXiv version has had an inconsequential update in 2023), and a venue should be indicated (I'd suggest NeurIPS rather than arXiv)\n- Other articles are missing a venue or source\n- Several articles have incorrect capitalization in the title due to the lack of curly braces, e.g., use `{CodeT5}` to avoid it being rendered as \"Codet5\"." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. The paper argues that preceding-instruction attention helps avoid reuse of the same register (e.g., \"eax\") immediately after it is used in the previous instruction. However, this motivation is questionable because it does not explain how further subsequent instructions are prevented from reusing the same register. A more straightforward solution could be achieved with inter-instruction attention, as it can attend to all previous instructions, which raises the concern of functional overlap between preceding-instruction attention and inter-instruction attention, thus potentially making the preceding-instruction attention redundant.\n2. While Nova-1B and Nova-6B are much larger than CodeART, their performance gains in BCSD are limited. For example, in the k=100 case, CodeART sees a 17% improvement over JTrans with attention regularization, but Nova's improvement is only marginal, from 0.76 to 0.78 (as shown in Table 12). This suggests that adding hierarchical attention and other inductive biases provides limited benefits when scaling the model, and Tables 11-14 show that removing hierarchical attention does not lead to significant performance drops, questioning its overall necessity. And also in Table 5, the improvement brought by contrastive learning is much higher than the Hierarchical Attention.\n3. In the paper's analysis of attention distribution (Figure 10), the standard attention frequently converges on the first token, a phenomenon known as attention sink [1]. This behavior is also evident in the analysis of hierarchical attention (Figure 10(c, d)), where each token strongly attends to the first token within its attention mask, specifically the [INST-(x-1)] token, which represents the summary of the previous instruction. But it is not common when human try to interpret the functionality of each individual instruction. Furthermore, the justification for the Hierarchical Attention Mechanism —which selectively uses specific attention heads to represent the best attention maps—is somewhat ad hoc and lacks a clearer rationale. \n4. The Hierarchical Attention Mechanism introduced in this paper represents a strong inductive bias; however, the underlying insights behind this inductive bias are not clearly explained. Additionally, the mechanism bears a striking resemblance to the Attention Regularization used in CodeART, with the primary difference being the absence of Preceding-Instruction Attention in CodeART. The effectiveness of this additional attention component has also been called into question earlier in the reivew, casting some doubt on its true contribution to the overall performance.\n5. While the use of contrastive learning aligns well with the BCSD task—improving performance by ensuring that functionally similar binaries, even across different optimizations, are represented similarly—it's less clear how this objective enhances the model's ability in decompliation. The training goal focuses on increasing the similarity of tokens from the same function but compiled with different optimization settings. However, this doesn't seem directly aligned with the ultimate goal of recovering executable source code, which requires more precise structural and semantic understanding beyond just token similarity across optimization levels. It would be greatly appreciated if the authors could provide some intuition as to why this approach can lead to improvements in decompliation.\n6. The authors introduced a novel optimization contrastive learning approach for the BCSD task, which had not been previously applied in the previous works, which commonly use the InfoNCE loss (line 220) or the triplet loss. As it is not discussed with deeper detail in the paper, it raises the question of whether these gains are substantial enough to justify the added complexity and whether this approach could be effectively generalized to improve other models in BCSD tasks.\n\n[1]: Efficient Streaming Language Models with Attention Sinks" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper is well-structured and easy to follow. Concepts such as hierarchical attention and contrastive learning are clearly explained.\n2. The paper proposes a new method for encoding assembly code by using a Hierarchical Attention Mechanism to effectively capture the semantics of assembly instructions, while employing Contrastive Learning to ensure that functionally equivalent assembly code, even at different optimization levels, is represented similarly. This novel combination allows the model to robustly understand and learn from diverse assembly code structures.\n3. The paper conducts a broad range of experiments across multiple tasks and datasets, providing comprehensive evidence of the model’s effectiveness.\n4. Despite its specialized focus on assembly code, Nova's hierarchical attention is compatible with standard self-attention mechanisms, allowing it to seamlessly integrate and benefit from advancements in base models and code generation models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents Nova, a generative language model specifically crafted for understanding and generating assembly code. Nova integrates hierarchical attention mechanisms with contrastive learning to effectively capture the semantics of code. The hierarchical attention mechanism focuses on intra-instruction, preceding-instruction, and inter-instruction relations, while contrastive learning ensures that functionally equivalent code, even with different optimizations, is similarly represented. The model is evaluated on two key tasks: decompilation (recovering high-level source code from assembly) and binary code similarity detection (BCSD) (measuring the similarity between binary code functions). Nova shows superior performance in both tasks, excelling in decompilation by accurately generating source code from optimized assembly, and achieving high recall in BCSD by effectively identifying similar code across different optimization levels." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Unclear motivation for introducing several inductive bias by Hierarchical Attention Mechanism. While the added attention mask inductive bias shows promising results in the BCD task, its impact in the BCSD task is minimal. This discrepancy raises questions about why the inductive bias performs well in one task but fails to offer significant improvements in the other.\n2. Lack of Design Discussion. The paper lacks sufficient discussion on key design components like Preceding-Instruction Attention and Optimization Contrastive Learning (CL). Without Preceding-Instruction Attention, the attention design is quite similar to CodeART, raising questions about the novelty and contribution of the approach." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "For binary similarity detection, compilers may inline functions or eliminate them altogether. How does your approach handle such scenarios?\n\nIf additional information (e.g., execution traces) were provided to GPT, or if iterative interaction with GPT were allowed, could the proposed approach still outperform a GPT-based model?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Clear Writing and Novel Application: The paper is well-written and easy to follow. The idea of applying hierarchical attention to assembly code is interesting and novel. While hierarchical attention is commonly used in NLP tasks, applying this mechanism to assembly code is, to the best of my knowledge, unprecedented.\n\n2. Promising Results: The evaluation results are promising. Nova demonstrates substantial improvements in both decompilation accuracy and similarity detection compared to existing models, validating its approach with strong experimental evidence." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents Nova, a generative language model specifically designed for assembly code, addressing unique challenges posed by the low information density and diversity in assembly syntax due to compiler optimizations. Nova introduces a hierarchical attention mechanism and employs contrastive learning to improve the model's understanding of assembly semantics across diverse optimization levels. Trained on a large assembly corpus, Nova outperforms existing techniques in tasks like binary code decompilation and binary code similarity detection, showing improvements in Pass@1 and Recall@1 rates over state-of-the-art models." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Generalizability: The model is trained exclusively on x86 assembly code, which may limit its generalizability to other assembly languages, such as ARM or MIPS.\n\nRealism of Evaluation Settings:\n\n(1) The decompilation prompt requires optimization level information, but it is unclear if this information is accessible in stripped binaries.\n\n(2) For baseline models like GPT, fine-tuning with additional data isn’t necessary, raising questions about the fairness of the comparison. If GPT were given a few-shot learning setup or fine-tuned using OpenAI’s API, could it still be outperformed by the proposed approach?\n\n\nRelated Work: The paper omits discussion of several relevant works, which could provide a broader context for its contributions.\n\n[1] Debin: Predicting Debug Information in Stripped Binaries. CCS 2018\n\n[2] {DIRE}: A Neural Approach to Decompiled Identifier Renaming. ASE 2019\n\n[3] Learning to Reverse DNNs from AI Programs Automatically. IJCAI 2022\n\n[4] Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization. S&P 2019\n\n[5] Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection. CCS 2017.\n\n[6] ecompiling x86 Deep Neural Network Executables. Security 2023." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please check my concerns in the weakness section." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "Strengths:\n+ The topic is interesting and important, addressing large language model (LLM) comprehension of assembly code.\n+ The paper is well-structured and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a generative model, Nova, tailored for assembly code tasks. Nova employs a hierarchical attention mechanism and is trained using contrastive learning objectives. This paper evaluates its effectiveness on two assembly code tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Weaknesses:\n- Comparison may be unfair due to different fine-tuning practices.\n- Evaluation of individual components is insufficient.\n- Generalization assessment is lacking.\n \n(1) Unfair Comparison: Nova is evaluated on two tasks, with fine-tuning applied specifically for each. However, the baseline models (such as Table 2) do not undergo the same fine-tuning for the tasks, leading to a potentially unfair comparison.\n \n(2) Component Evaluation: Nova’s hierarchical self-attention mechanism consists of three components, yet the paper lacks detailed performance assessments for each part. Despite a reasonable design, their individual impact remains unexamined.\n \n(3) Contrastive Learning Objectives: The contrastive learning objectives contain two distinct components. Further evidence is necessary to substantiate the utility of each objective. Additionally, the contrastive learning approach depends on the available optimization levels. Handling unseen optimization levels at inference should be discussed.\n \n(4) Normalization Process: In the data collection section, a normalization step is applied, but its relevance or benefit to Nova’s training is unclear.\n \n(5) Results across different optimization levels should be explored—e.g., training on O0, O1, O2 and testing on O3.\n \n(6) Random Sampling in BCSD Task: The BCSD task employs random sampling, yet statistical results are missing. Reporting such results would reduce the impact of randomness on performance claims." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. See weakness 1,2 \n2. Could you provide more details about how to construct $F$ in practice used in Functional CL?\n3. The authors state that hierarchical attention is only applied to half of the attention head. Since different attention heads can learn different features, I wonder if this setup is robust to the selection of the attention heads?\n4. Would the pre-trained models (Nova-1B, 6B) be public available?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. LLMs for binary code is an important topic to study\n2. This work proposes new methods to train Nova based on the properties of assembly code, which is clearly motivated.\n3. The proposed models show a clear improvement in binary code decompilation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents Nova, a generative LLM for assembly code. To effectively learn from assembly with low information density, it uses a novel hierarchical attention mechanism which combines intra-instruction attention, preceding-instruction attention and inter-instruction attention. It further utilizes contrastive learning to better learn the semantics of assembly code from different optimization levels. The authors demonstrate the superiority of Nova over the baselines on binary code decompilation and code similarity detection tasks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The comparison on code similarity detection may not be fair. For example, CodeArt uses 12 transformer blocks with 768 hidden dimensions, whose size is smaller than Nova-1B. The authors should compare Nova with the baseline under a similar size with the same pre-training data to demonstrate the superiority of Nova on code similarity detection. For the current result, we can find that compared with CodeArt, Nova actually does not show a significant improvement (e.g. both are 0.64 for Nova-1B under K=500). So it is in question whether Nova is indeed better for code similarity detection.\n2. The experiments for Comparison with Techniques Handling Long Input are confusing. Specifically, it has the following problems:\n\na) What is \"Nova’s Fine-Tuning\" in Table 3? It seems Nova does not have something special in terms of fine-tuning. Does it just mean fine-tuning with hierarchical attention or also with Nova's pretraining as suggested in Line 360? \n\nb) What is the average token length for downstream tasks before truncation? The authors want to claim Nova is better at solving long input challenges. But I see from the Appendix that Nova uses the input length as 1024 tokens during pre-training and 2048 for fine-tuning. It may be hard to claim this length to be \"long-context\". Considering that assembly code should be much longer than source code and Granite-3B-Code-128K can handle 128K input tokens at most, have you tested in the benchmarks where the input context is longer, e.g. 8k/32k/128k?\n\n3. The presentation of the paper can be improved. Specifically, a) Line 281 is unclear. The authors should clearly state that their pre-training contains two stages and the loss in Line 240 is used in the second stage. b) The ablation study should be separated into new subsections instead of mixing with Section 4.1 c) The equations are not numbered." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024nova,\ntitle={Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4ytRL3HJrq},\nnote={under review}\n}" }, "abstract": { "value": "Binary code analysis is the foundation of crucial tasks in the security domain; thus building effective binary analysis techniques is more important than ever. Large language models (LLMs) although have brought impressive improvement to source code tasks, do not directly generalize to assembly code due to the unique challenges of assembly: (1) the low information density of assembly and (2) the diverse optimizations in assembly code. To overcome these challenges, this work proposes a hierarchical attention mechanism that builds attention summaries to capture the semantics more effectively and designs contrastive learning objectives to train LLMs to learn assembly optimization. Equipped with these techniques, this work develops Nova, a generative LLM for assembly code. Nova outperforms existing techniques on binary code decompilation by up to 14.84 -- 21.58% higher Pass@1 and Pass@10, and outperforms the latest binary code similarity detection techniques by up to 6.17% Recall@1, showing promising abilities on both assembly generation and understanding tasks." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "large language model", "hierarchical attention", "contrastive learning", "assembly code" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/05395eaeba223c6be1b073a356ee472843dad3f0.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "- Given that DeCo's effectiveness depends on selecting an optimal layer range (e.g., 20-28), does the layer range need tuning for different MLLMs?\n- Could you provide more details on the selection process for the 500 images used in experiments? Additionally, which split(s) were used to determine and evaluate the hyperparameters, and were any specific criteria applied for these selections?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The authors demonstrate through probing experiments that MLLMs can recognize objects in earlier layers but tend to “forget” this information due to language model priors in deeper layers, leading to hallucinations. This insight offers a novel layer-wise perspective on the hallucination mechanism in MLLMs.\n- The figures illustrating token probabilities across transformer layers effectively highlight the trends for hallucinated versus non-hallucinated tokens, making the analysis accessible and informative.\n- Compared to existing methods like VCD and OPERA, DeCo achieves similar or better hallucination suppression with lower latency overhead, enhancing its practicality for real-world applications.\n- Evaluation across diverse benchmarks (CHAIR, POPE, and MME) and several models (InstructBLIP, MiniGPT-4, LLaVA-1.5, and Qwen-VL) provides a well-rounded assessment of DeCo’s effectiveness." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduces DeCo (Dynamic Correction Decoding), a decoding technique to mitigate hallucinations in Multimodal Large Language Models (MLLMs). The authors identify that MLLMs are capable of recognizing objects in earlier layers, but this recognition is suppressed in deeper layers by strong language model priors, which leads to hallucinations. DeCo dynamically integrates the output from preceding layers, which contain higher probabilities for ground-truth tokens, into the final layer logits to enhance visual grounding and suppress hallucinations. Experimental results on datasets such as CHAIR, POPE, MME and GPT-4o assisted evaluation demonstrate DeCo’s significant improvements over baselines in hallucination suppression across multiple MLLMs, with manageable latency increases, highlighting its practical applicability." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- In Figure 9, the response includes awkward repetition, with \"The horse statue is positioned on top of the chair\" stated multiple times. This raises questions about the effectiveness of the chosen α\\alphaα value in avoiding repetitive language, as the authors indicated that high \\alpha values could increase repetition.\n- In Figure 10, DeCo reduces a significant hallucination (misidentifying a lift as a \"chair\"), but the output still contains a hallucination about \"several other people visible in the background.\" This discrepancy between benchmark performance and qualitative examples suggests that DeCo’s effectiveness might not fully translate into consistently accurate real-world responses.\n- For each time step tt, language tokens that are not related to the visual input but are essential for sentence generation may be influenced as they pass through the proposed method. There appears to be a lack of investigation into the nature of this influence." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "My primary concern lies in the potentially low quality of text generated from the preceding layers. I will be happy to raise my score if my current questions and concerns can be addressed." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The motivation seems interesting.\n2. The paper is well written and easy to follow. The diagrams are essential to understanding this paper.\n3. This paper achieves good results on existing datasets.\n4. The main technical pipeline is clear." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper demonstrates that while MLLMs may produce incorrect target outputs in the final layer, they effectively recognize visual objects in the preceding layers. The authors propose a dynamic correction decoding method for MLLMs (DeCo), which adaptively selects relevant preceding layers and proportionally integrates their knowledge into the final layer to adjust the output logits. The proposed method outperforms existing approaches on public datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Although the experiments indicate improved performance in preceding layers, I am concerned about the coherence and richness of the text generated at these stages. Could you provide further evaluation metrics for text quality, such as BLEU or other relevant scores?\n2. In Figure 1(b), the interval [10, 20] appears optimal, yet in Figure 7(b), [17, 28] shows better performance. Could you clarify this discrepancy?\n3. Could you provide more evidence to demonstrate how dynamic soft modulation prevents abrupt changes in logits? Additional ablation studies might further substantiate this claim.\n4. Could you share detailed MME results to highlight the method's performance across different subtasks?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- It's unclear to me how is the affine layer $\\phi$, in L104, initialized and trained, if at all? If it needs training, then it seems each layer needs such a layer. If it doesn't, then how do we make sure that resentation across layers can share the same mapping to present token probability?\n- POPE evaluates answers in Yes/No. How could decoding strategy have impact on the performance for this benchmark?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- This work makes an interesting observation of how visual information exists in intermediate layers, and then overridden by knowledge prior closer to the output\n- The proposed mitigation method is lightweight and efficient.\n- The experimental results are in general better than baselines." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper makes the observation that MLLMs' internal prior suppresses the visual information, thus leading to hallucination. Besides, they empirically observe that intermediate layers may have less such suppression. Motivated by this observation, this work proposes to combine intermediate logits with final layer projection, and demonstrate improvement in reducing hallucination via empirical study." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although the presentation has a focus about image-conditioned generative language model, the methodology for finding 1 and 2, as well as the proposed layer selection and probability correction, are modality agnostic. The findings are mostly empirical, and it's unclear whether this is a general phenomenum for other models in the same size, nor for models in other sizes. \n\nThere has been quite a few literature in studying LLM's internal presentation and hallucination, only selectively listing a few as [1-5] . What the multi-modal setting brings is the strong conditional dependency, while for text-only use cases there might or might not be informative conditions. An analytical comparison on how an MLLM focuses or ignores input conditions can be more informative and persuasive in supporting the methodology. \n\nIn L191-L201 the paper compares the token output with and without the image condition. However this has been studied thouroughly in [6], which also proposes hallucination detection and mitigation method.\n\nThe method design also seems ad-hoc, there are thresholds in Eq2 and Eq3, layer interval a, b in Eq4 and the weight $\\alpha$ in Eq7. Together they contribute to amplifying the concern in the generalizability of the proposed method.\n\nI suggest to connect the empirical evidences in this paper to 1/ evidences from other papers with the same spirit, and 2/ the unique property and behavior of conditional multi-modal modeling. \n\n**Reference**\n\n[1] Liu, Junteng, et al. \"On the Universal Truthfulness Hyperplane Inside LLMs.\" arXiv preprint arXiv:2407.08582 (2024).\n\n[2] Li, Kenneth, et al. \"Inference-time intervention: Eliciting truthful answers from a language model.\" Advances in Neural Information Processing Systems 36 (2024).\n\n[3] Zhang, Tianhang, et al. \"Enhancing uncertainty-based hallucination detection with stronger focus.\" arXiv preprint arXiv:2311.13230 (2023).\n\n[4] Azaria, Amos, and Tom Mitchell. \"The internal state of an LLM knows when it's lying.\" arXiv preprint arXiv:2304.13734 (2023).\n\n[5] Duan, Hanyu, Yi Yang, and Kar Yan Tam. \"Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States.\" arXiv preprint arXiv:2402.09733 (2024).\n\n[6] Favero, Alessandro, et al. \"Multi-modal hallucination control by visual information grounding.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- Could you clarify whether Finding 1 in Section 2.1 in the section is related to the methodology of the paper? It seems that embedding-level knowledge wasn't used to assist the model.\n- Could you follow LLAVA’s setting and conduct more extensive evaluations on comprehensive benchmarks like MMbench and MMVet, given their importance for assessing the model's overall performance?\n- Could the authors clarify the specific settings followed in the experiments presented in Table 3? How do these settings differ from those used in LLaVA?\n- Is this decoding method useful in more advanced VLLMs, such as Qwen-VL, VILA, etc.?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper provides a detailed examination of why MLLMs generate non-existent objects, offering valuable insights into the hallucination issue in image captioning tasks.\n\n- The introduction of DeCo is innovative, using preceding-layer knowledge to reduce hallucinations during inference, effectively improving output accuracy.\n\n- The method of probing across transformer layers reveals how hallucinations emerge in later layers, helping to understand MLLM behavior better." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper investigates why MLLMs generate hallucinations, particularly in image captioning tasks, and introduces DeCo, an innovative method that leverages knowledge from earlier layers to reduce hallucinations during inference. However, I still have some concerns about this article, specifically in regard to the weaknesses.\nIf these concerns are addressed, I will consider raising my score." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- I am confused by the experimental results about POPE in Table 3, as they do not seem to fully align with the result from LLAVA 1.5. \n- The authors did not perform more extensive evaluations on comprehensive benchmark such as MMbench and MMVet, which are crucial for assessing the model's overall performance." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "A dynamic correction decoding method for MLLMs (Deco), which adaptively selects the appropriate preceding layers and proportionally integrates knowledge into the final layer to adjust the output logits for hallucination mitigation." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024mllm,\ntitle={{MLLM} can see? Dynamic Correction Decoding for Hallucination Mitigation},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4z3IguA4Zg},\nnote={under review}\n}" }, "abstract": { "value": "Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood. In this paper, we present an empirical analysis and find that, although MLLMs incorrectly generate the targets in the final output, they are actually able to recognize visual objects in the preceding layers. We speculate that this may be due to the strong knowledge priors of the language model suppressing the visual information, leading to hallucinations. Motivated by this, we propose a novel dynamic correction decoding method for MLLMs (Deco), which adaptively selects the appropriate preceding layers and proportionally integrates knowledge into the final layer to adjust the output logits. Note that Deco is model agnostic and can be seamlessly incorporated with various classic decoding strategies and applied to different MLLMs. We evaluate Deco on widely-used benchmarks, demonstrating that it can reduce hallucination rates by a large margin compared to baselines, highlighting its potential to mitigate hallucinations." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Hallucination Mitigation", "Multimodal Large Language Models", "Decoding Strategy" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/5243a318d6418f4b3733970695d24fa42b894334.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/82f20fe4945f9a0c88e0318d790a9d9f7053bee8.zip" }, "title": { "value": "MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": null, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": null, "primary_area": null, "questions": null, "rating": null, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": null, "summary": null, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": null, "withdrawal_confirmation": { "value": "I have read and agree with the venue's withdrawal policy on behalf of myself and my co-authors." } }, { "TLDR": null, "_bibtex": { "value": "@misc{\nzhang2024selfexploring,\ntitle={Self-Exploring Language Models: Active Preference Elicitation for Online Alignment},\nauthor={Shenao Zhang and Donghan Yu and Hiteshi Sharma and Han Zhong and Zhihan Liu and Ziyi Yang and Shuohang Wang and Hany Hassan Awadalla and Zhaoran Wang},\nyear={2024},\nurl={https://openreview.net/forum?id=4zQ5eIPtMp}\n}" }, "abstract": { "value": "Preference optimization, particularly through Reinforcement Learning from Human Feedback (RLHF), has achieved significant success in aligning Large Language Models (LLMs) to adhere to human intentions. Unlike offline alignment with a fixed dataset, online feedback collection from humans or AI on model generations typically leads to more capable reward models and better-aligned LLMs through an iterative process. However, achieving a globally accurate reward model requires systematic exploration to generate diverse responses that span the vast space of natural language. Random sampling from standard reward-maximizing LLMs alone is insufficient to fulfill this requirement. To address this issue, we propose a bilevel objective optimistically biased towards potentially high-reward responses to actively explore out-of-distribution regions. By solving the inner-level problem with the reparameterized reward function, the resulting algorithm, named Self-Exploring Language Models (SELM), eliminates the need for a separate RM and iteratively updates the LLM with a straightforward objective. Compared to Direct Preference Optimization (DPO), the SELM objective reduces indiscriminate favor of unseen extrapolations and enhances exploration efficiency. Our experimental results demonstrate that when fine-tuned on Zephyr-7B-SFT and Llama-3-8B-Instruct models, SELM significantly boosts the performance on instruction-following benchmarks such as MT-Bench and AlpacaEval 2.0, as well as various standard academic benchmarks in different settings." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": { "value": [ "~Shenao_Zhang1", "~Donghan_Yu2", "~Hiteshi_Sharma1", "~Han_Zhong1", "~Zhihan_Liu1", "~Ziyi_Yang1", "~Shuohang_Wang1", "~Hany_Hassan_Awadalla1", "~Zhaoran_Wang1" ] }, "authors": { "value": [ "Shenao Zhang", "Donghan Yu", "Hiteshi Sharma", "Han Zhong", "Zhihan Liu", "Ziyi Yang", "Shuohang Wang", "Hany Hassan Awadalla", "Zhaoran Wang" ] }, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Online Alignment", "Large Language Model" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": { "value": "zhang|selfexploring_language_models_active_preference_elicitation_for_online_alignment" }, "pdf": { "value": "/pdf/068bd2cbc229b13a233275af3792af892675429a.pdf" }, "presentation": null, "primary_area": { "value": "alignment, fairness, safety, privacy, and societal considerations" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/d7aaa3fdf6770dc66cb3895afd2fc7369615f10c.zip" }, "title": { "value": "Self-Exploring Language Models: Active Preference Elicitation for Online Alignment" }, "venue": { "value": "ICLR 2025 Conference Withdrawn Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Withdrawn_Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I like the paper overall and don't have any major questions to ask." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. A novel training strategy that replaces frozen layer parameters with a fusion of parameters from neighboring layers, controlled by two learnable parameters, reducing the training load without compromising performance.\n2. Replacement Learning shows substantial savings in memory usage and training time and achieves better accuracy than end-to-end training.\n3. The method performs well across diverse architectures (CNNs and ViTs) and datasets, suggesting broad applicability.\n4. Extensive experiments on benchmark datasets confirm the effectiveness of Replacement Learning in surpassing the performance of standard training approaches." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces Replacement Learning, a novel training technique aimed at reducing the number of learnable parameters in deep learning models while maintaining or even enhancing model performance. The approach specifically targets the limitations of traditional end-to-end training, such as high computational demand, memory usage, and parameter redundancy, which are common in deeper architectures. Rather than updating all parameters during backpropagation, Replacement Learning freezes certain layers and uses parameters from adjacent layers, controlled by two learnable parameters, to inform the frozen layers through a parameter integration mechanism. This design enables the frozen layers to leverage both local and global feature representations, balancing historical context with new inputs while reducing memory and computational costs.\n\nThe authors conduct experiments across multiple image classification datasets (CIFAR-10, STL-10, SVHN, and ImageNet) using various architectures, including CNNs and Vision Transformers (ViTs). Results demonstrate that Replacement Learning reduces GPU memory usage, training time, and the number of parameters, while achieving higher accuracy than traditional end-to-end training. Furthermore, the method shows versatility, adapting effectively across different architectures and datasets." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I like the paper overall. However, would like to point some weaknesses which the authors have also mentioned in their limitations section:\n1. While effective on image-based tasks, the approach has not yet been tested on other domains such as NLP or multimodal tasks, which limits its generalizability.\n2. The paper could benefit from a more in-depth discussion of any limitations associated with freezing certain layers and its impact on long-term learning dependencies, especially in deeper networks." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. How does the value of $k$ impact the performance of the models? The author should perform an ablation study on this value.\n\n2. How does the proposed method compare with other parameter-efficient training methods?\n\n3. How does the proposed method compare with other alternative backpropagation methods and methods utilizing surrounding layers during training?\n\n4. Can this method be applied to fine-tuning pre-trained networks?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper is clearly written and is generally easy to follow.\n\n2. The problem being studied in the paper is becoming increasingly important recently.\n\n3. The idea is simple yet seems to be something that people haven’t tried before." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes an efficient training method for deep neural networks, named Replacement Learning, which aims to reduce the number of trainable parameters, training time, and memory consumption. Replacement Learning achieves this by selectively freezing the parameters of certain layers, which then utilize parameters from adjacent layers updated through a parameter integration mechanism controlled by just two learnable parameters. This method leverages information from surrounding structures to enhance overall model performance while reducing computation and conserving GPU memory." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The proposed method only marginally reduces the GPU memory consumption and training time compared to the baseline training method. \n\n2. The paper did not compare the proposed method with any other parameter-efficient training methods, such as [3].\n\n3. Although the paper discusses related works on alternative backpropagation methods and training utilizing surrounding layers, none of the related works are compared with the proposed methods in the experiments.\n\n4. Parameter-efficient training methods [1, 2] are widely applied in fine-tuning pre-trained networks by selectively updating a small subset of model parameters, streamlining the adaptation process of pre-trained models, and facilitating rapid deployment across various domains. However, this paper only studies the setting for training-from-scratch.\n\n[1] Zhang, Taolin, et al. \"Parameter-efficient and memory-efficient tuning for vision transformer: a disentangled approach.\" ECCV 2024.\n\n[2] He, Xuehai, et al. \"Parameter-efficient model adaptation for vision transformers.\" AAAI 2023.\n\n[3] Mostafa, Hesham, and Xin Wang. \"Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization.\" ICML 2019." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "I must express my disappointment after spending several hours reviewing such a submission.\n\nI suggest that the authors reconsider the motivation behind the proposed method and conduct all experiments with greater seriousness and care, as many results currently seem unreasonable." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "I’d say the idea is somewhat interesting, and the paper follows the ICLR submission style." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this work, the authors aim to improve network efficiency and reduce parameter redundancy by introducing Replacement Learning, a straightforward approach that fixes the parameters in a layer by interpolating between two adjacent layers. The experimental results indicate that this method is effective—although I find these results somewhat difficult to believe." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Figure 1 doesn’t make any sense—using very large models on very small datasets. It would be more meaningful to test on a larger dataset like ImageNet, with different models of different scales for comparison.\n\n2. what is the motivation of such a method?\n\n3. There are several unsupported and arbitrary statements in the paper. For example, in Line 83, \"Considering that parameters from adjacent layers, if solely derived from either shallow or deep layers, often fail to simultaneously enable frozen layers to excel in learning both local and global features\" lacks justification. The relationship to local or global features is unclear here, as adjacent layers don’t inherently correspond to shallow or deep feature characteristics.\n\n4. In Eq. 9, the parameters of the i-th layer are a linear combination of those from the previous and next layers. What’s the motivation behind? It’s also unclear why this setup would yield better performance, as shown in Table 1 and Table 2.\n\n5. What is the motivation for providing detailed information about backpropagation? I don’t see any differences or novel contributions in this section.\n\n6. In the experiments, I don’t understand how reducing the number of parameters in a model can consistently improve performance across different datasets and models. This seems completely unreasonable and doesn’t make sense at all, especially I didn't see anything can help with this.\n\n7. BTW, the results on CIFAR-10, SVHN, and STL-10 aren’t reliable, as these datasets are too small to provide meaningful insights.\n\n8. Why is k=4 chosen? There should be an ablation study to justify this choice. Additionally, why is there a frozen layer every k layers throughout the networks? I assume we can select layers.\n\n9. What happens if the frozen layer is the last layer in a ResNet stage? How would parameter interpolation be handled in this case?\n\n10. The experimental settings MUST have issues. All the results for the vanilla models are SIGNIFICANTLY lower than original papers. This calls into question the reliability of the results presented in the paper, potentially not only ImageNet.\n\n11. Typo： feature maps can be found in Figure 4.3.1. \n\n12. I cannot tell any essiential differences among the four images in Fig. 3." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See above" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The writing of the article is quite good and the article is logical.\n2. The experiments on the classification task seem to be adequate.\n3. The charts and graphs are more beautiful and properly presented.\n4. Supplementary materials are substantial." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a method of replacement Learning, which reduces computational overhead and resource consumption in deep learning. It enhances model performance while surpassing end-to-end training in efficiency and memory usage. The method has been validated on various datasets and architectures, showing its versatility and potential for broader application." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Title: The authors use \"Visual Tasks\" in the title. \"Visual Tasks\" include multiple tasks such as classification, detection, segmentation, etc., but it seems that the paper is only validated on the classification task. I suggest adding other tasks to the paper, as has been done in several recent PEFT works [1-3].\n[1] 1% vs 100%: Parameter-efficient low rank adapter for dense predictions.\n[2] Pro-tuning: Unified prompt tuning for vision tasks.\n[3] Adapter is all you need for tuning visual tasks.\n[4] Parameter-efficient is not sufficient: Exploring parameter, memory, and time efficient adapter tuning for dense predictions.\n\n2. Related work: the authors perhaps left out some of the most recent work of parameter-efficient fine tuning (PEFT). \n3. Experiments: (1) Experiments are performed only on classification tasks; (2) More parameter-efficient fine-tuning methods are available for comparison.\n\nI would consider increasing the score if the authors could provide more convincing comparative experiments." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "I would improve my rating if experiments are performed for points 1 and 4 in weakness and clearing my questions in points, 3 and 5.\n\nI also have a minor question:\n While it is clear both $\\theta_{i-1}$ and $\\theta_{i+1}$ are useful for approximating $\\theta_i$ why not expand it to $\\theta_{i-n}$ and $\\theta_{i+n}$, this would increase the global context of the computations with minimal increase in parameters?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Replacement Learning introduces a novel approach for training more efficient models with lesser number of parameters. The parameter integration has good future potential, especially when applied to structured pruning and upon supporting transfer learning.\n\n2. The experiments show good performance for image classification tasks for both convolutional and transformer networks for accuracy, memory and time for 1 epoch.\n3. The paper presents ablations to determine the robustness of the method and also presents complexity analysis of the method.\n4. The paper addresses the most experimental parameters in the appendix, exhibiting good reproducibility." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper introduced a neural network learning mechanism - Replacement Learning that replaces the parameters of selected layers with just 2 parameters a,b. The output of that layer is computer by a linear model a* activations of previous layer + b* activations of next layer. Given that similar layers in neural network produce correlated outputs, this linear combination approximates the replaced layer's outputs. The method reduces the parameter count, throughput and increases accuracy for image classification datasets(CIFAR-10,STL-10,SVHN and Imagenet-1k)." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The primary concern with the paper is that the experiments are conducted by training networks from scratch. Transfer learning boosts the performance of classification tasks, for instance in the official vit paper[1], VIT B/16 upon transfer learning on CIFAR10 reached an accuracy of 98.13% while the paper achieves only 72.86%, therefore it is important to perform an experiment for examining the effect of replacement learning when transfer learnt. One possible reason why the paper omits trying transfer learning can be initialization of replaced parameters. Parameter Integration $a * \\theta_{i-1}+ b* \\theta_{i+1}$ is a linear model, this allows approximating a,b values for pre-trained layers in few steps. \n\n2. The paper only shows results on classification, effect of replacement learning on downstream tasks such as object detection and segmentation would strengthen the approach.\n\n3. The flow of gradients is not in a singular direction during replacment learning as suggested by Eq. 15 where $\\delta_{i}$ is used for gradient computation of $\\delta_{i+1}$. This could cause issues such as vanishing or exploding gradients and must be given a look at. \n\n4. While the experiments show better time per epoch, the effect of replacement learning on convergence must be studied. This is important as time for 1 epoch can be misleading when the number of epochs to converge is significantly higher than backpropagation.\n\n5. While the paper addresses that only MSA layers were chosen for replacement, where $\\theta_{i-1}$ and $\\theta_{i+1}$ also MSA layers? This is important as [2] does not guarantee correlation between MSA and MLP layers. \n\nNitpick: some figure references are wrong, Figures in the ablation display figure numbers 4.3.1, 4.3.2 and 4.3.3 which need to be corrected to figure 3,4,5. Usually it is the placement of caption and label in \\begin{figure} that causes this issue.\n\nReferences:\n[1] Dosovitskiy, Alexey. \"An image is worth 16x16 words: Transformers for image recognition at scale.\" arXiv preprint arXiv:2010.11929 (2020).\n[2] Venkataramanan, Shashanka, et al. \"Skip-attention: Improving vision transformers by paying less attention.\" arXiv preprint arXiv:2301.02240 (2023)." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024replacement,\ntitle={Replacement Learning: Training Vision Tasks with Fewer Learnable Parameters},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=4zygH3k8Zr},\nnote={under review}\n}" }, "abstract": { "value": "Traditional end-to-end deep learning models often enhance feature representation and overall performance by increasing the depth and complexity of the network during training. However, this approach inevitably introduces issues of parameter redundancy and resource inefficiency, especially in deeper networks. While existing works attempt to skip certain redundant layers to alleviate these problems, challenges related to poor performance, computational complexity, and inefficient memory usage remain. To address these issues, we propose an innovative training approach called Replacement Learning, which mitigates these limitations by completely replacing all the parameters of the frozen layers with only two learnable parameters. Specifically, Replacement Learning selectively freezes the parameters of certain layers, and the frozen layers utilize parameters from adjacent layers, updating them through a parameter integration mechanism controlled by two learnable parameters. This method leverages information from surrounding structures, reduces computation, conserves GPU memory, and maintains a balance between historical context and new inputs, ultimately enhancing overall model performance. We conducted experiments across four benchmark datasets, including CIFAR-10, STL-10, SVHN, and ImageNet, utilizing various architectures such as CNNs and ViTs to validate the effectiveness of Replacement Learning. Experimental results demonstrate that our approach reduces the number of parameters, training time, and memory consumption while completely surpassing the performance of end-to-end training." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Machine Learning", "Deep Learning", "Foundation Models" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c973a7f54d76355ee653aae74c5fea389c0277f1.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/56ed34603fcdcc2c5d321d8d99c5577b28189532.pdf" }, "title": { "value": "Replacement Learning: Training Vision Tasks with Fewer Learnable Parameters" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1. The tightness for the upper and lower bounds in Lemma 1 is determined by the choice of p. By considering the simple model where f(x) outputs the same logit for all classes, the ratio between the upper and lower bound is minimized by $p=\\infty$. Is is a better choice to consider $\\| f(x) \\|_\\infty = \\max_k | f_k(x) |$?\n\n2. The usage of exp transformation of logits in the softmax function appears pivotal to the proof of Theorem 1. And if we take b(x) as a general non-negative function of f(x), the upper bound may reduce to 1. And minimizing the entropy of opinion in equation 6 can also lead to the Dirac distribution, which is over-confident. Is there a characterization for the class of functions that could be used to form a reasonable belief b(x)?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "This paper accurately spots the paradox of EM's learning objective: minimization of entropy leads to over-confidence. And the paper proposes a simple yet effective solution to minimize entropy with respect to a probability distribution that faithfully estimates the uncertainty without over-confidence. It is a very reasonable idea to differentiate between the statistics used for prediction and for uncertainty estimation, which has long been considered the same in the TTA literature. Therefore, the algorithm enjoys the feature that the entropy minimization can be tailored to samples with different uncertainty, which is also supported by the monotonicity result in Theorem 1. The introduction of SL for uncertainty estimation is natural and perfectly compatible with softmax functions used for training models in most cases. As a result, the implementation is light-weight, model-agnostic, and extremely easy to embed into any TTA algorithms based on the EM objective. The experiments are convincing by covering both standard TTA tasks and more challenging settings of open-world TTA. A surprisingly significant 34.5% improvement on accuracy is reported on the model of SAR. And the algorithm has further addressed uncertainty estimation under continual distribution shift as a side product, which itself is also an important problem." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper addresses the model collapse of the popular Entropy Minimization algorithm for Test-Time Adaptation. Motivated by the observation that the amplification of model over-confidence causes model collapse, this paper proposes to minimize entropy with respect to an augmented output distribution that includes the probability to reject a sample, which is an uncertainty estimation technique known as subjective logic. Moreover, a logit normalization is designed in order to avoid degenerated solutions. Theoretical analysis reveals that the resulting approach upper bounds model confidence during adaptation based on the sample-specific confidence of the initial model. The resulting algorithm, COME, can be easily embedded into general EM-based TTA methods with a few lines of code revision. Experiments across TTA, open-world and life long TTA settings demonstrate a significant and consistent improvement upon current EM baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "These are not necessarily weaknesses but rather some questions that I would like to confirm with the author.\n1. How does the algorithm ensure that $b_k$ is non-negative for the computation of entropy, since $b_k$ is implemented as \n$(e^{f_k(x)}-1)/ \\sum_{k'} e^{f_k'(x)}$ which could be negative?\n\n2. Why does the algorithm keep $u$ close to $u_0$? Does it imply that the uncertainty estimation for the pretrained model is trusted? What if the pretrained model is over-confident? What about the alternative constraint $u \\geq u_0$ which seems to be more conservative as is the objective of COME?\n\n3. Average false positive rate is used in experiments to assess uncertainty estimation. However, FPR measures the correctness of uncertainty estimation with such a binary perspective: for the samples we predict 1, what is the actual proportion of 0. Uncertainty estimation considers a more sophisticated question: for the samples we predict with a probability 0.7, is the actual proportion of 1 exactly 0.7? Expected calibration error (ECE) is a better metric in this sense.\n\n4. Are there standard errors of the reported results?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please refer to weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- This paper is well-motivated, and the story makes sense. \n- Extensive experiments have been done to support the proposed method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose Conservatively Minimizing Entropy, a method for test-time adaptation (TTA) that improves test-time adaption by managing prediction uncertainty. Unlike traditional entropy minimization, which can lead to overconfidence, COME uses a Dirichlet distribution to model uncertainty, allowing the model to avoid forced classification on unreliable data." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My major concerns include:\n- For the proposed method: Why Dirichlet distribution is used? How is the Dirichlet distribution related to the final algorithm in Algorithm 1. In addition, what is the role of delta in Algorithm 1? It seems that the authors tell a long story about their algorithm, but the algorithm itself is rather simple.\n- For the theoretical analysis: Could the authors provide a more detailed (theoretical) comparison between the proposed method and traditional EM? What is the benefit?\n- For baselines: Some baselines are missing, for example, [1] and [2].\n- For the datasets: I'm curious why the authors follow literatures on outliers detection.\n- For Theory 1: What is the exact benefit of the upper bound of model confidence? I think it will also hurt the performance on some \"confident\" samples. \n\n[1] Nado, Z., Padhy, S., Sculley, D., D'Amour, A., Lakshminarayanan, B., & Snoek, J. (2020). Evaluating prediction-time batch normalization for robustness under covariate shift. arXiv preprint arXiv:2006.10963.\n[2]Zhou, A., & Levine, S. (2021). Bayesian adaptation for covariate shift. Advances in neural information processing systems, 34, 914-927." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "see weakness" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The proposed algorithm introduces a rejection mechanism for unreliable samples in the TTA process, preventing the model from learning from potentially noisy labeled data. It is simple to integrate into existing TTA frameworks, and the experimental results indicate satisfactory performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the issue of model collapse in entropy minimization algorithms for test-time adaptation. The authors propose a novel entropy minimization approach that models prediction uncertainty by defining a Dirichlet prior distribution over model predictions. This method regularizes the model to favor conservative confidence for unreliable samples. Experiments on benchmark datasets demonstrate the effectiveness of the proposed algorithm." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The proposed method and its theoretical analysis rely heavily on existing techniques, which limits its technical novelty.\n\nThe core concept shares some similarity with research on learning with rejection. It is recommended to discuss how the proposed loss function compares with the loss functions used in learning with rejection, as outlined in [1].\n\nThere is a lack of experiments involving real-world applications with distribution shifts, as exemplified in [2]. Testing the proposed algorithm on real-world data streams in dynamic environments is suggested to validate its robustness.\n\nReferences:\n\n[1] Cortes, Corinna, Giulia DeSalvo, and Mehryar Mohri. \"Learning with rejection.\" Algorithmic Learning Theory (2016).\n\n[2] Yao, Huaxiu, et al. \"Wild-time: A benchmark of in-the-wild distribution shift over time.\" Advances in Neural Information Processing Systems 35 (2022): 10309-10324." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- (Eq. (6)) What if a hyperparameter $\\lambda \\in \\mathbb{R}$ is introduced as $-\\sum_{k=1}^K b_k \\log b_k - \\lambda u \\log u$? This seems to be a straightforward generalization of Eq. (6).\n\n- Is there any quantitative correspondence between $\\tau$ and $\\delta$ (Eq. (9) & (7))?\n\n- How can we set $p$ and $\\tau$ (or $\\delta$) in practice?\n\n- To my understanding, the regularizer (Eq. (9)) effectively prevents spurious training that would drive the second term in Eq. (6) to zero by enforcing $e \\rightarrow 0$. Is this correct?\n\n- To me, the proposed method seems to be a simple combination of known techniques (to clarify, I do *not* claim that simplicity alone is grounds for rejection at all). Could you clarify the differences of the proposed technique from other Bayesian approaches, confidence calibration algorithms, semi-supervised learning, and entropy regularization techniques? A quantitative and objective discussion would be preferable, which would significantly enhance the paper's contribution and clarity." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The motivation of the proposed method is clear. \n\n- The idea of transforming the optimization problem with a constraint (Eq. (7)) into a simpler form (Eq. (9)) is interesting and effective, which significantly simplifies the problem.\n\n- Several theoretical results, as well as empirical results, are provided. Theorem 1 helps quantitative understanding of the proposed method.\n\n- Code is available." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes a Bayesian inference technique to address the overconfidence problem in test-time domain adaptation. Experiments demonstrate its effectiveness, achieving a SOTA performance." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Experiments on the dependence of the results on $p$ and $\\tau$ are lacking. I would like to see the results and discussions when $p \\neq 2$ and $\\tau \\neq 1$.\n\n- (Major) The paper applies a Bayesian inference (Eq. (5)) with a regularization to the overconfidence problem inherent in TTA. The \"EM\" (entropy minimization) mentioned in the paper is, in a more general context, the unsupervised learning using soft pseudo-label, which has a wide variety of applications beyond TTA.\nWhile the proposed method is technically sound, it would benefit from discussion in a broader context—such as unsupervised learning with a pretrained model, unsupervised domain adaptation, source-free domain adaptation, and semi-supervised learning—to emphasize its wide applicability as a key contribution.\n\n- (Major) Error bars are missing. Could you provide error bars because several performance gains are marginal?\n\n- The performance metric \"Avg.\" in the tables are nonsense, while it is actually a bad convention in the field. Obviously, a \"1% gain\" is quite different in iNaturalist and SSB-Hard, for example.\n\n- (Minor) A large part of the paper is dedicated to reviewing previously known works, such as the overconfidence problem, which hinders readability.\n\n- (Minor) Much of the paper, particularly the description of the proposed method, is redundant. A more direct tone is generally preferable in academic writing.\n\n- Overall, while the paper is well-written and the proposed method is interesting and effective, the paper would benefit from a more refined articulation of its contributions and focus. Additionally, the reproducibility issue should be addressed." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose an alternative to entropy minimization as a better learning principle for TTA tasks." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024come,\ntitle={{COME}: Test-time Adaption by Conservatively Minimizing Entropy},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=506BjJ1ziZ},\nnote={under review}\n}" }, "abstract": { "value": "Machine learning models must continuously self-adjust themselves for novel data distribution in the open world. As the predominant principle, entropy minimization (EM) has been proven to be a simple yet effective cornerstone in existing test-time adaption (TTA) methods. While unfortunately its fatal limitation (i.e., overconfidence) tends to result in model collapse. For this issue, we propose to conservatively minimize the entropy (COME), which is a simple drop-in replacement of traditional EM to elegantly address the limitation. In essence, COME explicitly models the uncertainty by characterizing a Dirichlet prior distribution over model predictions during TTA. By doing so, COME naturally regularizes the model to favor conservative confidence on unreliable samples. Theoretically, we provide a preliminary analysis to reveal the ability of COME in enhancing the optimization stability by introducing a data-adaptive lower bound on the entropy. Empirically, our method achieves state-of-the-art performance on commonly used benchmarks, showing significant improvements in terms of classification accuracy and uncertainty estimation under various settings including standard, life-long and open-world TTA. Our code is available at: \\href{https://anonymous.4open.science/r/anonymous-9F46}{https://anonymous.4open.science/r/anonymous-9F46}." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Test-time adaption", "Out-of-distribution generalization" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c5c5247d760fe1da2b23102c3b3a9956b72078bb.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/f250a93f733dc54853d2348b9dcd81fb248abd50.zip" }, "title": { "value": "COME: Test-time Adaption by Conservatively Minimizing Entropy" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please refer to my comments above." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper is well-organized. The proposed model is evaluated on multiple tasks including general visual understanding benchmarks, VQA, and video datasets. Ablation study and runtime evaluation are also provided." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents an MLLM architecture to improve the multi-granularity visual understanding abilities of multimodal models. The method follows the idea proposed in Mini-Gemini to fuse high and low-resolution visual encoders and adds object recognition from other foundation models to enhance object-level understanding ability. A series of models ranging from 3.8B to 34B are proposed and the models are evaluated on multiple popular benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The technical contribution of the paper is not very significant. The paper claims the main contribution is combining low, high-resolution, and object-level features. But the design of combining low and high-resolution features mainly comes from mini-Gemini and some modifications on the fusion module are proposed in the paper. The introduction of object-level features requires extra models and makes the base architecture more complex. \n\n- I am not convinced about the necessity of introducing the extra object-level information. Recent state-of-the-art MLLMs like Qwen2 VL [r1], LLaVA-Onevision [r2] and Pixtral [r3] all adopt a single encoder solution. I think the MLLMs themselves should have the ability to capture object-level information from the images with sufficient data and a proper training strategy. The method proposed in the paper may reduce the requirement for training data but the more complex architecture also makes the solution less general. Besides, the method also didn't show large improvements on SEED (69.4 vs. 68.9) and MMStar (35.1 vs. 37.6) compared to Mini-Gemini with the same base LLM. \n\n- Many recent MLLMs like [r2, r4] are not compared. Compared to these methods, the proposed solution is not strong enough and imore complex. \n\n[r1] Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution\n\n[r2] LLaVA-OneVision: Easy Visual Task Transfer\n\n[r3] Pixtral 12B\n\n[r4] Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "The concerning questions are stated in the weakness section. Based on the weaknesses listed above, I lean to reject this manuscript at its current version." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The key claim for the paper that multi-granularity features with low-res, high-res, and object features can improve detailed understanding and object recognition skills is reasonable. The authors design the conv-gated fusing module and demonstrate its effectiveness through complete ablation studies.\n2. The series of models and benchmarks are clear and complete. The authors train the variants for MG-LLaVA based on Phi, Vicuna, LLaMA3, and Yi1.5 and conduct experiments on various multi-modal benchmarks. \n3. The overall architecture of the paper is well-structured and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper proposes MG-LLaVA to improve the capability for recognizing multi-granularity features for current MLLMs and flatten the restriction of the resolution in visual inputs. MG-LLaVA includes the low-resolution, high-resolution, and object-level features altogether and fuses them with a conv-gate fusion module for the general visual features. The object ROIs are extracted by a pre-trained detection model for better object-level understanding skills. The paper proposes a series of MLLMs ranging from 3.8B to 34B based on various LLMs and shows strong performance across image and video multimodal benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The idea of fusing multi-granularity features is not novel, as integrating low-resolution and high-resolution images has been demonstrated effect by a range of works, including LLaVA-NeXt, LLaVA-HR, Mini-Gemini, LLaVA-UHD, etc. The difference in MG-LLaVA lies in the usage of detected objects. However, the detection operation introduces extra computational costs and external models with extra information, which is not an optimal solution. \n2. The performance comparisons against existing MLLMs are relatively weak. For example, the results of MG-LLaVA equipped with Vicuna-7B do not surpass baselines with similar efforts on some benchmarks, including SQA, TextVQA, MMStar, etc. The overall number of training data is significantly heavier than the baselines (2.6M), which makes the comparisons more unfair. \n3. Some key ablations seem lacking. The authors are encouraged to clearly show the contributions for performance with every part of the visual feature to show the difference between previous works." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. The performance improvement on similarly sized LLMs in Table 2 and Table 3 appears modest.\n2. The ablation study would benefit from visual comparisons to illustrate the impact of each component, such as case studies or visualizations of feature-level effects.\n3. Some failure cases should be shown to provide insights into the method’s limitations.\n4. It is unclear if the method can handle larger images, such as 1024p or 2k resolutions." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The integration and fusion of multi-granularity features with object-centric features is novel for MLLMs.\n2. Experimental results demonstrate the effectiveness of the proposed pipeline.\n3. The paper is well-written and clearly presented." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a novel Multimodal Large Language Model (MLLM) that improves visual processing capabilities by incorporating a multi-granularity vision flow, which includes low-resolution, high-resolution, and object-centric features. This approach enhances the performance of current Large Language Models (LLMs), as demonstrated in the experiments." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The performance improvement on similarly sized LLMs in Table 2 and Table 3 appears modest.\n2. The ablation study would benefit from visual comparisons to illustrate the impact of each component, such as case studies or visualizations of feature-level effects.\n3. Some failure cases should be shown to provide insights into the method’s limitations.\n4. It is unclear if the method can handle larger images, such as 1024p or 2k resolutions." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1. The SEEDBench mentioned in the article uses SEEDBench-Image, but I checked the scores for leaderboard and the other methods mentioned in the paper, and they seem to correspond to SEEDBench-Avg (which contains both video and image), so it's not clear to me whether the comparison here includes scores from the video task.\n2. If an open vocabulary detector is used, why is a tagger used to determine the bounding box instead of generating ROI directly based on text embedding?\n3. The article suggests that this approach is intuitively better for small target comprehension or counting tasks, are there any datasets in this area that show that this approach has more significant performance gains on specific tasks?\n4. I found that the Monkey model uses a similar idea to enhance the performance of the model and also proposes to augment the data with traditional CV methods for refinement, is there a comparison to this approach in the paper? For example, changing the base LLM to Qwen-7b to compare with Monkey (Li et al. 2023) and more models on this field.\n\n[1] Li, Zhang, et al. \"Monkey: Image resolution and text label are important things for large multi-modal models.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The goal of this paper is to release the power of MLMs on fine-grained tasks. A high resolution visual encoder is introduced to make up for the complement of previous work. And some fusion and compression strategies are introduced to ease the computational pressure. In addition to this, the article demonstrates that this new framework achieves significantly higher scores on MLMs at several scales, which fully demonstrates the effectiveness of the method. Moreover, this is the first approach to introduce object-level features in the field of MLMs, and experimentally, the article demonstrates the ability of their method to achieve higher scores than private models under MMBench and SEEDBench." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper aims to improve the visual capabilities of MLMs (multimodal large models) by proposing a new model MG-LLaVA. limited to resources, most of MLMs nowadays just have low resolution inputs, which are challenging on fine-grained tasks. Therefore, this papet proposes a novel framework that introduces object-level feature in addition to high resolution visual encoder. Based these, the article also uses a gating-based fusion strategy as well as explicit integration on object-level feature. These approaches reduce the computational pressure introduced by high resolution images and simultaneously improve performance on fine-grained tasks. On MMBench and SEEDBench, the model outperforms even the private models GPT-4V and GeminiPro-V. The article also conducts extensive experiments to show that their framework achieves competitive scores on multiple datasets of images or videos." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. As mentioned in the article itself, the introduction of multi-granularity and multi-scale to enhance model performance is a common approach to convolutional networks, and merely migrating this approach to the field of MLMs is hardly an innovative contribution. Some of the algorithms used in the article from object detection only do some information enhancement on the input side, while many MLMs can already accomplish the object detection task by themselves nowadays.\n2. The scores achieved on both the MMBench as well as SEEDBench datasets, while respectable, are not compared to some of the more competitive models. I identified MMB as version 1 and SEEDBench as Avg based on the scores of Qwen-VL and MiniCPM-V2, and there are a number of scores on both leaderboards that are higher than the scores of MG-LLaVA work, eg. Honeybee (Cha et al., 2024), AllSeeing-v2 (Wang et al. 2024) based on Vicuna-13b at MMB-test. and then you can also find a lot of similar models with higher scores on the same substrate.\n3. In addition to Perception Benchmarks. this problem can also be found in Visual QA and Video QA. such as on the MSRVTT-QA dataset. there are already many models with very high scores in 2024. Some of them also use some methods to improve the model's ability on fine-grained tasks. eg. Flash-VStream (Zhang et al. 2024) Monkey (Li et al. 2023). The article does not seem to compare these new 2024 models.\n\nTo summarize, I think the approach proposed in the article is valid, but MG-LLaVA does not do the job of making a difference, either from an innovation perspective or from a performance perspective.\n\n[1] Cha, Junbum, et al. \"Honeybee: Locality-enhanced projector for multimodal llm.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024.\n\n[2] Wang, Weiyun, et al. \"The all-seeing project v2: Towards general relation comprehension of the open world.\" *arXiv preprint arXiv:2402.19474* (2024).\n\n[3] Zhang, Haoji, et al. \"Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams.\" *arXiv preprint arXiv:2406.08085* (2024).\n\n[4] Li, Zhang, et al. \"Monkey: Image resolution and text label are important things for large multi-modal models.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Although the method performs well on the general VQA, it lacks a comprehensive assessment of fine-grained perception capabilities. It would be more fair and convincing to compare it with region-level methods like Next-Chat and Osprey on the RefCOCO dataset. This could be accomplished by using the bounding box of the corresponding target as input.\n\n2. It is evident that using object-level features can enhance the perception ability of MLLMs. However, incorporating additional detectors introduces extra computational costs and biases. An equitable efficiency comparison is necessary. if these added costs surpass the benefits from data expansion, parameter extension, or data filtering, it results in negative optimization, as I believe is the case with MG-LLaVA. From the performance comparison, when using Vicuna 7B, MMStar exhibits lower performance than other models, indicating data leakage risk and validating the risk of bias introduced by reliance on detectors.\n\n3. Although MG-LLaVA shows improvements in general capabilities, these enhancements are marginal. The added expense of using additional detection models and object-level features should yield a greater performance boost. Moreover, during inference, reliance on detection results from other models is cumbersome. Transforming external dependencies into self-mining processes could significantly enhance the practical utility of the model." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The structure of paper is simple and easy to read, and the model implementation is very easy to follow.\n2. The idea is very straightforward, and the experiments are solid. It is reasonable to introduce multi-granularity object-level features to enhance the perceptual capabilities of Multimodal Large Language Models (MLLMs)." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Summary:\n\nMG-LLaVA is a multi-modal large language model (MLLM) designed to improve visual processing capabilities by using a multi-granularity vision flow. This includes low-resolution, high-resolution, and object-centric features to enhance perception tasks requiring detailed visual information. Extensive experiments have validated its effectiveness. \n\nContributions:\n\n1. Leveraging an additional open vocabulary detection model introduces multi-granularity object-level features to enhance the perceptual capabilities of MLLMs.\n2. Extensive experiments demonstrate the effectiveness of the method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The idea appears incremental, as it simply integrates high-resolution image interpretation with region-level image understanding, resembling a trick \n2. Experimental evaluations and fair comparisons are notably lacking. Given that multi-granularity features are utilized to augment the model's perceptual abilities, evaluations should be conducted on fine-grained perception datasets. General VQA is inadequate for assessing the fine-grained perceptual capabilities of MLLM. \n3. Excessive reliance on additional detector inputs may result in suboptimal, non-end-to-end outcomes." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024mgllava,\ntitle={{MG}-{LL}a{VA}: Towards Multi-Granularity Visual Instruction Tuning},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=50RNY6uM2Q},\nnote={under review}\n}" }, "abstract": { "value": "Multi-modal large language models (MLLMs) have made significant strides in various image comprehension tasks. However, the majority of these models are constrained to processing low-resolution images, which limits their effectiveness in perception tasks that necessitate detailed visual information. In our study, we present MG-LLaVA, an innovative MLLM that enhances the model's visual processing capabilities by incorporating a multi-granularity vision flow, which includes low-resolution, high-resolution, and object-centric features. We propose the integration of an additional high-resolution visual encoder to capture fine-grained details, which are then fused with base visual features through a Conv-Gate fusion network. To further refine the model's object recognition abilities, we incorporate object-level features derived from bounding boxes identified by offline detectors. Being trained solely on publicly available multimodal data through instruction tuning, MG-LLaVA demonstrates exceptional perception skills. We instantiate MG-LLaVA with a wide variety of language encoders, ranging from 3.8B to 34B, to evaluate the model's performance comprehensively. Extensive evaluations across multiple benchmarks demonstrate that MG-LLaVA outperforms existing MLLMs of comparable parameter sizes, showcasing its remarkable efficacy." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Multi-Modality", "Large Language Models" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/f96b774be6db03a91f2bee5864a092daa9c52f55.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/05c109cae2229d273c5122d918632087800118f3.zip" }, "title": { "value": "MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see Weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Developing a multi-domain explanation method is intriguing, and the discussion of key challenges is reasonable.\n2. The manuscript is well written. It is easy to follow this work.\n3. The experiments conducted with the proposed methods are adequate." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper present the Wavelet Attribution Method (WAM), which improves gradient-based feature attribution by utilizing the wavelet domain as a comprehensive framework for explaining classifiers across various domains. The findings indicate that WAM meets or surpasses SOTA metrics for faithfulness, effectively identifying not only the locations of significant features but also highlighting their structural patterns." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. I believe there is a lack of sufficient baselines. It would be helpful to include more options such as LIME, SHAP, and concept-based explanations for image and audio data. Since there is no quantitative evaluation in 3D settings, adding 3D LIME, SHAP, sensitivity analysis, and Layer-wise Relevance Propagation (LRP) for 3D baselines would be a solid starting point.\n\n References:\n\n [1] \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier \n [2] A Unified Approach to Interpreting Model Predictions \n [3] Towards Automatic Concept-based Explanations \n2. The experiments were conducted on only one dataset; therefore, it would be essential to include results from several datasets.\n3. In the audio results (Figure 1 and Figure 4), it is quite challenging to identify the areas being explained. Making the less important areas grayscale while highlighting the significant areas in red would improve interpretability.\n4. It would have been better to conduct a human study for the qualitative evaluation. For example, utilizing Amazon Mechanical Turk (MTurk) to ask annotators to evaluate WAM while providing explanations for other baselines would be beneficial." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Could you provide a more rigorous definition of 'structural components' and clarify how they differ from standard features in the context of explainability? Specifically, can we establish any meaningful relationships between these components based on their implicit structure?\n\n2. Since inter-scale dependencies are central to your claims, what specific dependencies does the wavelet domain preserve, and how does this preservation impact attribution in practice? For example, in the case of image explanations, presenting different scales of explanations does not seem to provide substantial additional insight.\n\n3. To what extent have you included newer, state-of-the-art models in your evaluation, and how might WAM perform with models developed after 2017, considering the rapid advancements in explainability techniques? Have you considered expanding the method to a self-explainable framework by introducing a novel loss term directly in the wavelet domain?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "WAM brings an intriguing approach by utilizing wavelet decomposition to improve gradient-based feature attribution methods. The method leverages the mathematical properties of the wavelet domain, potentially addressing limitations of saliency maps that flatten hierarchical and spatial relationships. This could provide meaningful explanations by capturing features across multiple scales. In theory, WAM’s emphasis on inter-scale dependencies could enhance explainability across images, audio, and 3D shapes, offering an innovative view on XAI. Additionally, by unifying SmoothGrad and Integrated Gradients, WAM capitalizes on established approaches while potentially broadening their applicability across multiple modalities. This multimodal capability, though perhaps overstated, should be a promising generalization that is not commonly found in the comparison methods, which are often restricted to single data domains." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper presents the Wavelet Attribution Method (WAM), a novel explainability approach for deep learning models in the wavelet domain. Unlike traditional pixel-based attribution methods (saliency maps), WAM leverages the structural benefits of the wavelet domain, offering a more generalizable approach that applies to various data modalities, including images, audio, and 3D shapes. WAM decomposes the model’s decisions by analyzing how features in the wavelet-transformed space affect predictions. The method integrates gradient-based attribution techniques with wavelet decomposition to capture both where (location) and what (content) aspects of the data structure. Through empirical evaluation, WAM demonstrates superior performance on faithfulness and fidelity metrics for image and audio data, achieving enhanced clarity in the model’s decision-making process." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Despite its ambitious goals, WAM introduces several ambiguities and potential oversights. Key among them is the unclear use of 'structural components', a term the paper uses to describe feature-level insights that the method claims to provide. This concept, critical to WAM’s claims of 'what' explainability, lacks a clear definition or grounding in quantifiable relationships among components, making it difficult to ascertain whether these are indeed structural features rather than just relevant input attributes. Furthermore, while wavelet decomposition is introduced as a novel approach to attribution, the practical interpretability of multi-scale heatmaps remains underexplored in the paper. it is unclear how users can derive specific insights from these maps without a more explicit explanation. WAM’s assertion of state-of-the-art (SOTA) performance is another potential weakness, given that its comparisons rely largely on 2017 models like SmoothGrad, Grad-CAM, and Integrated Gradients, raising questions about whether the method is genuinely competitive in the context of more recent advancements in the XAI field. Additionally, the effectiveness of the faithfulness metrics used to benchmark WAM’s performance could benefit from further clarification, especially given the method’s claims of surpassing existing techniques across domains." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "Potential plagiarism: this paper's approach is almost identical with the paper \"Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domain\" which presented at workshop \"XAI in Action: Past, Present, and Future Applications workshop at NeurIPS 2023\". I understand that workshop papers are not considered as formal archived publications. However, I have some concerns about potential plagiarism, as it is unclear whether the authors of the current submission are the same as those of the workshop paper." }, "flag_for_ethics_review": { "value": [ "Yes, Research integrity issues (e.g., plagiarism, dual submission)" ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "In Figure 2 (d), I don’t know why decomposing important coefficient at each scale is necessary. Is there specific reason that scaling is important?\n\nDoes WAM can discriminate each object when multiple classes are at one image, such as Cat-dog image?\n\nWhat is GRADWCAM in figure 5?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper is well-structured, presenting its concepts clearly and in an accessible manner. The theoretical foundations for using gradients in the wavelet domain are well-developed, filling a gap in the current literature where such an approach has not been extensively explored.\n\n The paper introduces a novel method by leveraging gradients in the wavelet domain, which provides a new perspective on feature attribution.\n\nThe paper's evaluation of the proposed method's faithfulness using multiple faithfulness metrics is thorough and valuable. By comparing the proposed approach across different evaluation criteria, the authors demonstrate the robustness and reliability of their method.\n\n The figures and visualizations are well-designed, enhancing the clarity of the paper. They effectively illustrate the principles of the Wavelet Attribution Method (WAM) and provide a clear understanding of how wavelet-based attributions differ from traditional pixel-based methods. The use of visual examples makes the theoretical concepts more accessible and supports the argument for the method’s efficacy." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces the “Wavelet Attribution Method(WAM),” a feature attribution technique in explanable AI that attiburion is on wavelet domain. WAM leverages the wavelet domain to extend gradient-based feature attributions, preserving the multi-scale structures of the input data. This approach provides a unified framework applicable across various modalities such as images, audio, and 3D shapes. Empirical evaluations demonstrate that WAM matches or surpasses other methods in faithfulness metrics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The approach used in this paper shares similarities with the WaveletX(Kolek et al, 2022) method, which also performs saliency mapping in the wavelet domain. The primary distinction lies in the use of gradients as a mask in this work, While WaveletX optimize the mask on wavelet domain. However, this difference may not be significant enough to constitute a radical contribution to the field. It would be better to explicitly compare both methods and highlight the novel aspects of WAM.\n\nThe paper does not include quantitative assessments for 3D shape analysis and relies solely on qualitative results. Incorporating quantitative metrics would strengthen the evaluation and provide a more comprehensive understanding of the method's performance in this domain.\n\nAlthough the authors claim that the Wavelet Attribution Method (WAM) outperforms other approaches across different domains, the results in Table 2 suggest otherwise. Specifically, WAM does not consistently outperform Integrated Gradients, indicating that the performance advantage may not be as significant as claimed.\n\nThe experimental comparisons primarily involve methods like Integrated Gradients, GradCAM++, and SmoothGrad, which are not the most recent or best-performing approaches according to the fidelity metric. Including comparisons with more recent and state-of-the-art methods, such as LRP-αβ (Samek et al., 2016), LayerCAM (Jiang et al., 2021), Guided Backpropagation (Selvaraju et al., 2016), AttnLRP (Achibat et al., 2024), and SRD (Han et al., 2024), would strengthen the evaluation and better demonstrate WAM's superiority.\n\nThe paper does not adequately demonstrate how WAM enables an understanding of what features the model uses to make decisions. While the method highlights important wavelets, it does not clarify the specific meaning or relevance of these wavelets to the classification task. For instance, approaches like CRAFT (Concept Recursive Activation FacTorization for Explainability, Fel et al., 2023) offer more explicit explanations by identifying meaningful concepts (e.g., \"elephant tusk\") that recur across multiple samples. Providing a similar level of interpretability by linking important wavelets to specific semantic features would improve the explanatory power of WAM." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. Could you clarify why many results in Table 3 are zero when using the Faithfulness metric?\n2. Could you explain why Integrated Gradients perform best on the \\mu-Fidelity metric but perform worst in Faithfulness? Is this discrepancy due to different experimental setups?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "1. The topic addressed in this paper is important, and the proposed method is novel. Current attribution methods struggle to explain and distinguish structural components in the input, and the integration of wavelets into attribution calculations shows promise in addressing this limitation.\n2. Extensive evaluations are conducted across multiple modalities, including images, audio, and 3D shapes.\n3. This paper is easy to follow, with detailed descriptions of evaluation metrics and experiments." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors identified a gap in existing attribution methods, specifically their inability to explain the structural components of input data. The authors propose a novel wavelet-based attribution method extending to multiple modalities including images, audio, and 3D shapes. While the topic is timely and the problem addressed is of significant importance, the proposed method lacks a clearly demonstrated advantage in explaining structural components compared to existing techniques. Moreover, the quantitative results do not convincingly showcase the method's superiority. Therefore, I tend to reject this paper in its current version." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The primary weakness is the lack of significant differentiation between the proposed attribution method and existing approaches. The paper provides limited analysis or visualizations that convincingly show how WAM offers better hierarchical explanations. While some comparisons are presented (e.g., in Figures 2 and 12), I don’t see clear and enough advantages in explaining input structures. The authors should further explore or emphasize the distinctive aspects of their method.\n2. The quantitative results do not consistently demonstrate improvements over existing attribution methods. While comparisons with older methods are acceptable given the novelty of the proposed approach, the proposed method falls significantly behind in several key metrics, such as in Tables 3 and 5 (Appendix). Additionally, the results in Table 1 are concerning; given the definition of the Faithfulness metric in Eq. 9, the output should always be positive. Why, then, are many results in Table 3 reported as zero?\n3. The organization of the results section could be improved. For example, the discussion of perturbation-based attribution methods in Section 4.2 appears abruptly and feels disconnected from Section 4.1." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We generalize gradient-based explainability to the wavelet domain; expanding it to new modalities beyond images and show that it yields novel insights on model understanding.." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024one,\ntitle={One Wave to Explain Them All: A Unifying Perspective on Post-hoc Explainability},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=50UzaXh0gC},\nnote={under review}\n}" }, "abstract": { "value": "Despite the growing use of deep neural networks in safety-critical decision-making, their inherent black-box nature hinders transparency and interpretability. Explainable AI (XAI) methods have thus emerged to understand a model's internal workings, and notably attribution methods also called Saliency maps. Conventional attribution methods typically identify the locations - the where - of significant regions within an input. However, because they overlook the inherent structure of the input data, these methods often fail to interpret what these regions represent in terms of structural components (e.g., textures in images or transients in sounds). Furthermore, existing methods are usually tailored to a single data modality, limiting their generalizability. In this paper, we propose leveraging the wavelet domain as a robust mathematical foundation for attribution. Our approach, the Wavelet Attribution Method (WAM) extends the existing gradient-based feature attributions into the wavelet domain, providing a unified framework for explaining classifiers across images, audio, and 3D shapes. Empirical evaluations demonstrate that WAM matches or surpasses state-of-the-art methods across faithfulness metrics and models in image, audio, and 3D explainability. Finally, we show how our method explains not only the where - the important parts of the input - but also the what - the relevant patterns in terms of structural components." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "interpretability", "feature attribution", "wavelet", "images", "audio", "3D shapes" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/e2c23f05d88b97d4fbd8d1f0eac52eaf0c099818.pdf" }, "presentation": null, "primary_area": { "value": "interpretability and explainable AI" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/c1ecfc8989db2ff4e828e769fc492ac4a0b928a5.zip" }, "title": { "value": "One Wave to Explain Them All: A Unifying Perspective on Post-hoc Explainability" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please respond to my above concerns.\n\nIn addition, I would request the authors to add theorems / propositions after Theorems 3.2 and 3.6, without any \\gamma_t and \\beta_t terms. Or more generally, with as few variables as possible." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The main strengths of the paper is the theory. I do believe the GP semi-bandit problems considered in this paper are important, and having regret bounds for the algorithms discussed in this paper is also useful. \n\nSpecifically, it is nice to see sub-linear regret bound for all three algorithms.\n\nFurthermore, I also believe that the general techniques developed here may be useful to derive regret bounds for other bandit settings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors derive Bayesian regret bounds for various algorithms applied to combinatorial volatile GP semi-bandit problems. Specifically, the authors derive regret bounds for 3 algorithms: GP-UCB, GP-BayesUCB, and GP-Thompson Sampling. In comparison to previous works, this is the first regret bound for GP-Bayes UCB, and in addition, extend the existing regret bounds for GP-UCB and GP-TS to infinite, volatile and combinatorial setting (which is also includes the popular contextual bandit setting).\n\nThe authors apply these algorithms to the problem of online energy-efficient navigation to demonstrate the performance of the various algorithms." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. I think the paper lacks some clarity, and the exposition can improve significantly. For example, it requires recalling previous literature to properly understand the set-up in Section 2.1: Is A a finite set? 2^A is the set of a all subsets of A? What happens when A is infinite as in Section 3.2? \n2. Though the dependency on T is sub-linear, I am not sure how to view the dependency on K. Especially in the infinite case. Are there any lower bounds for these settings? It is hard to view how good or bad the bounds are with lack of comparisons.\n3. Building on top of 2 above, I am curious to know if this is the best dependency on T you can get. I am used to seeing \\sqrt{T} regret bounds for bandit algorithms -- is this not achievable in such settings?\n4. I thought that the experimental section was too artificial. If the motivation is to solve the problem in best possible way, there are probably better ways of solving the problem (for example using RL), than naively applying the semi-bandit learning algorithms. If the point is to show the performance of various algorithms, a simple example would suffice. In my opinion, the addition of these experiments does not add any additional value to the paper, and does not change the fact that the papers main (only) contributions are the theoretical bounds." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1.\tConnection Between Theory and Empirical Results:\nThe online energy-efficient navigation application is a compelling demonstration of the framework’s practical utility. However, it would be helpful to clarify how the empirical results relate to the theoretical findings. Specifically, can the empirical results be used to verify or illustrate key observations from the theoretical analysis? If this connection is not direct, could you design controlled simulated experiments that more explicitly validate the theoretical regret bounds or insights?\n2.\tExtended Comparison in Table 1:\nIncluding the regret rates alongside the regret bounds in Table 1 would greatly enhance its utility. This addition would allow readers to quickly compare the performance of different algorithms in terms of their theoretical guarantees. An extended table with this information would provide a clearer overview of the contributions and situate the work more firmly within the existing literature.\n3.\tDiscussion of Theoretical Challenges:\nAs mentioned in the weaknesses, a dedicated section or paragraph discussing the theoretical challenges faced in deriving the regret bounds for GP-BayesUCB, GP-UCB, and GP-TS would add significant value. This discussion could cover aspects such as handling the volatility in combinatorial settings, managing the complexities introduced by semi-bandit feedback, or other technical hurdles specific to these algorithms." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1.\tClear and Structured Presentation:\nThe paper is well-written, with clear explanations and illustrations of the research gaps. The novelty of this work is effectively communicated, making it accessible even to readers who may not be deeply familiar with the field.\n2.\tSolid Theoretical Contributions:\nThe authors provide rigorous theoretical analysis and establish new Bayesian regret bounds for multiple algorithms, including GP-BayesUCB, GP-UCB, and GP-TS. The paper addresses a significant gap in the literature by formalizing regret bounds for these settings. Full proofs are provided in the appendices, showcasing the depth of their analysis (though the correctness of these proofs was not verified).\n3.\tPractical Application:\nThe real-world application of their framework to online energy-efficient navigation is both relevant and interesting. It demonstrates the practical utility of their theoretical advancements and highlights the potential for real-world impact." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates the combinatorial volatile Gaussian process (GP) semi-bandit problem and provides the first Bayesian regret bounds for the GP-BayesUCB algorithm. In addition to this novel contribution, the authors extend their theoretical analysis to include Bayesian cumulative regret bounds for the GP-UCB and GP-TS algorithms, effectively addressing a notable research gap as highlighted in Table 1. To demonstrate the practical relevance of their framework, the authors apply their methods to a real-world problem: online energy-efficient navigation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1.\tLack of Discussion on Theoretical Challenges:\nWhile the paper provides new theoretical results, it does not clearly articulate the specific challenges encountered in deriving these results for GP-BayesUCB, GP-UCB, and GP-TS. A discussion on the theoretical hurdles and how they were addressed would provide valuable insight into the novelty and difficulty of these contributions.\n2.\tReproducibility Concerns:\nNo code is provided for the experiments. This absence raises concerns about the reproducibility of the empirical results." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See the weakness section" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper provides the bounds for the Bayesian regret for GP-BUCB, GP-UCB and GP-TS." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper claims to present a novel Bayesian regret bounds for GP-UCB and GP-TS in combinatorial, volatile and infinite arms setting. Further they present the experimental results for a real world application of online energy efficient navigation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Though the work claims to present the bounds for volatile case but the proof for the bounds do not seem to consider it. As an example what would happen when the best arm is not present among the observed arms?\n2. Not significant contribution, the paper mainly builds on the works of Russo & Roy 2014, Srinivas et al 2012 and Takeno et al 2023, where in to compute the Bayesian regret one only needs to compute the expectation over the high probability regret bounds given by the above works.\n3. Lemma 3.1 the results are considered for different regimes of horizons for different cases of the ratio, why not choose the limits as 1 to T for the 3rd case, wouldn't that be a tighter bound?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "Does the type of directed graph affect the applicability of the framework? For instance, how does the graph [being cyclic or acyclic] affect the performance of the framework? \n\nI did not notice any discussion in the paper about possible extensions and future directions and further impacts of their research, not even in the supplamentary section. Why? Can you please clarify?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper is building on top of other previously published frameworks, however, it is not a straightforward extension of the previous works. \n\nThe experiments (application of their framework) in online energy-efficient navigation problem seem to have added some novelties and value to the paper. \n\nThe paper is written in an excellent way. The explanations for the most important parts of the algorithms are clear. Also the similarities and differences (novelties) of their framework in comparison to the state-of-the-art is clarified properly." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper studies Gaussian process bandits in the contextual volatile semi-bandit setting. The contribution of the paper is mainly theoretical as it provides novel Bayesian regret bounds for previously designed algorithms. In addition, there is an interesting application of their framework in online energy efficient navigation. This experimental application builds on top of the previously designed experiment of the same application in bandit papers." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "No synthetic data experiment. Not even in the supplementary material. In my opinion, synthetic data experiments can significantly add to the development of intuitions about the framework. Also since you have much more control over the creation of the data, it can reveal interesting properties of the framework [in comparison with state-of-the-art]. \n\nAlso, I could not find an experiment with the horizon more than 500 rounds. I am curious about the performance of the frameworks as the horizon goes well beyond T=500. I believe that proper comparison of bandit frameworks [most of the times] comes with running the experiments for long horizons. \n\nI did not notice any discussion in the paper about possible extensions and future directions and further impacts of their research." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We present novel Bayesian regret bounds for GP-UCB, GP-BayesUCB and GP-TS for the combinatorial volatile Gaussian process semi-bandit problem and study the application of online energy-efficient navigation." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024bayesian,\ntitle={Bayesian Analysis of Combinatorial Gaussian Process Bandits},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=50cmx4SrkM},\nnote={under review}\n}" }, "abstract": { "value": "We consider the combinatorial volatile Gaussian process (GP) semi-bandit problem. Each round, an agent is provided a set of available base arms and must select a subset of them to maximize the long-term cumulative reward. We study the Bayesian setting and provide novel Bayesian cumulative regret bounds for three GP-based algorithms: GP-UCB, GP-BayesUCB and GP-TS. Our bounds extend previous results for GP-UCB and GP-TS to the \\emph{infinite}, \\emph{volatile} and \\emph{combinatorial} setting, and to the best of our knowledge, we provide the first regret bound for GP-BayesUCB. Volatile arms encompass other widely considered bandit problems such as contextual bandits.\nFurthermore, we employ our framework to address the challenging real-world problem of online energy-efficient navigation, where we demonstrate its effectiveness compared to the alternatives." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Multi-armed bandits", "Combinatorial bandits", "Contextual bandits", "Gaussian processes", "Energy-efficient navigation" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/ba1f9290622e1b80007f67f147daa53277598ee7.pdf" }, "presentation": null, "primary_area": { "value": "reinforcement learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Bayesian Analysis of Combinatorial Gaussian Process Bandits" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "### 1. Logic Behind the Benchmark Creation\n1. Since all LongHalQA data is generated by GPT-4V, isn’t Model X limited to analysing GPT-4V’s specific hallucinations rather than its own?\n2. Could Model X be missing its unique hallucinations because it doesn’t generate its own descriptions or conversations in LongHalQA?\n3. Wouldn’t a model evaluation approach where Model X generates its own text reveal more relevant hallucinations, as done in Kaul et al. and Jiang et al.?\n\n### 2. Lack of Details and Clarity\n1. How are dataset annotations and GroundingDINO used to filter the LongHalQA data? Can details on this process be provided?\n2. How are objects identified in the data? Are these from VG annotations, GPT-4V data, or both?\n3. Other than GroundingDINO, which image understanding tools are used, and how?\n4. If GPT-4V produces hallucination explanation pairs, is there manual verification, especially given its acknowledged hallucination issues (L298)?\n5. In what cases is human verification used for hallucination checking, and how does it impact the dataset?\n\n### Additional Clarity\n1. Can examples be given to the prompt templates in Appendix C to clarify instructions like \"Possible Content\" etc. (Figure 6)?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "This paper correctly identifies that many prior hallucination works focus on the narrow topic of object existence at an image level. To overcome they create questions which expand the evaluation to object level descriptions, object locations, attributes etc.\n\nTheir experimental results are numerous and allow the reader see the advantages/disadvantages of each model in the different types of question in LongHalQA (Table 2-5).\n\nThe authors make comparisons of their MCQ method to a free-form generation method in Section 6 and demonstrate the advantages of using MCQ over a free-form method in terms of efficiency of evaluation." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new benchmark for evaluating hallucinations in multi-modal large language models (MLLMs).\nThe paper makes use of GPT4V to generate image-level and object-level descriptions and conversation data for a set of images from VisualGenome. These wider range of generated data enables the proposed benchmark, LongHalQA, to evaluate various types of potential hallucination which go beyond the typical object level analysis (e.g. Is there a cat in the image?). The proposed method suggests two types of evaluation: (1) Hallucination Discrimination - the model must answer a MCQ about generated data (potentially containing hallucinations), to determine if the generated data contains hallucinations based on the image and the cause of the hallucination if present; (2) Hallucination Completion - the model must answer a MCQ, correctly selecting the answer which truthfully completes a partial conversation or description.\nThe authors conduct experiments on a range of open-source MLLMs and the closed-source GPT4o. They show that CoT prompting often has little or negative effect on results on LongHalQA. Finally they conduct a study in which hallucinations in free-form generations from their questions yield similar results to using their MCQ formulation." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I have two main weaknesses with this paper, unfortunately both of which I consider pretty major.\n\n### 1. The logic behind the creation of the benchmark itself.\n\nAs detailed in Section 4, all of the LongHalQA data comes from generations with GPT4V, this includes the descriptions, conversations etc. These generations are then analysed/modified with a number of checks. Furthermore, the question options themselves are generated with GPT4V. Therefore when evaluating a model X using LongHalQA, you are conditioning all reasoning/grounding/recognition of model X on the range of hallucinations GPT4V might make. This leaves a large range of potential hallucinations that are specific to model X which are left to be analysed, which may only be obtained by generating descriptions/conversations using model X rather than GPT4V. Taking Figure 1, GPT4V and the method used in Section 4 have created a hallucination regarding the number of people seated in the carriage. Now this is a hallucination of GPT4V + Section 4, _not_ of model X. Model X may have hallucinated the species of animal, the colour of the carriage etc, all of which is left potentially undiscovered because the hallucinations model X is asked to evaluate in LongHalQA are not its own, I therefore find the logic of this benchmark slightly confused. The free-form generations of methods like that of Kaul et al. and Jiang et al. referenced in the paper need the model being evaluated e.g. Model X to _actually generate_ its own text and therefore its own potential hallucinations.\n\n### 2. Lack of details and clarity.\n\nThe crucial step in this work is the generation of the data for LongHalQA, detailed in Section 4. I find this section to be extremely thin on details and lack clarity.\n1. L291 \"...then analyze and filter them based on dataset annotations and GroundingDINO...\", no information is given on how this process is done.\n2. L297, \"as illustrated in Appendix B.\" Appendix B contains a list of definitions of hallucinations used in this work.\n3. L303, \"Second, names of object present in the data are extracted, and certain image understanding tools such as GroundingDINO...\", there are no details on how objects present in the data are extracted, which data? VG annotations or names in the GPT4V generated data or both? Which image understanding tools other than GroundingDINO are used?\n3. L314-319, GPT4V is being used to generated hallucination explanation pairs, but there is no indication that manual checking is used here despite the authors accepting that GPT4V suffers from \"sever hallucinations\" (L298), the logic here seems confused on the ability of GPT4V to create such specific data which only contains one error which is also useful for evaluation.\n4. L320-L346, same arguments as above with the ability of GPT4V to this accurately.\n5. L344 \"except the hallucination checking that involves optional human verification\" does this mean human verification is used or not? What is the effect of using human verification in the data vs not?\n\nAdditionally as a more general point, the prompt templates used in Appendix C are extremely hard to follow without any examples, e.g. in Figure 6 what is \"Possible Content\"? The main text asks the reader to refer to Appendix C (L465) for details and then appears to simply paste the prompts used with no explanation of what goes where." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "No" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper is well-written and easy to follow. \n2. The motivation is reasonable and practical. I think this benchmark will accelerate the development of MLLMs on hallucination. \n3. The analysis of the experiment is relatively comprehensive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a long-context hallucination benchmark. This benchmark aims to solve two problems in the existing evaluation pipeline: it is too easy for discriminative tasks and too time-consuming for open-ended generative tasks. To achieve this, the authors propose the LongHalQA, which unifies discriminative and generative tasks as multi-choice problems. Also, they formulate the construction of LongHalQA as a pipeline to construct future hallucination benchmarks with long and complex questions and descriptions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. A little small number of evaluated models.\n2. No comparison between the performance of existing methods towards solving the hallucination of MLLMs. I'm interested in whether existing methods have improved on LongHalQA.\n3. Lack of related work about the method about how to decrease the hallucination of MLLMs" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. It is known that [LLMs are non-robust multiple-choice selectors](https://arxiv.org/abs/2309.03882). How do you tackle this problem during constructing this benchmark?\n2. #419 mentions the 'ranking-based accuracy` of Fuyu-8B, while I could not find the corresponding results in Table 4. It is a writing issue?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The proposed benchmark can contribute the further development of this field tackling and analyzing the hallucination of MLLMs.\n2. The proposed unification of discriminative question and generative question largely saves the evaluation cost via reducing the decoding sequence length." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new MLLM hallucination benchmark consisting of both hallucination discrimination and hallucination completion questions. The author unifies both discriminative and generative hallucination evaluation into the form of multiple-choice question where models only have to decode one token as response. The results show the proposed benchmark is challenging for both open-source MLLMs in varying sizes and strong GPT-4o." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Experimental results in Table 8 do not suggest a strong consistency between generation accuracy and mcq accuracy. For example, Fuyu-8b and LLaVA 1.5-7b exhibits score difference -12.41 in mcq while -41.0 in generation. It is necessary to include more methods into consideration, especially thous proposed to tackling hallucination of MLLMs such as LLaVA-RLHF, RLHF-V, Silkie and POVID.\n2. Hallucination pairs are generated by GPT-4V, which are prone to generate hallucinated visual description. The author have to explain how #317 controls the generation quality." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See weakness" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. LongHalQA addresses the limitations of previous benchmarks by creating a comprehensive dataset of hallucination text that mirrors real-world scenarios, providing a more accurate and complex testing environment for MLLMs.\n\n2. By eliminating the need for LLM evaluators, the benchmark ensures more stable and reliable results, avoiding the randomness and computational intensity associated with LLM-based evaluations.\n\n3. The combination of both discriminative and generative evaluation tasks in a multiple-choice format allows for a holistic assessment of MLLM performance in handling hallucinations, making the evaluation process more efficient." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper addresses the issue of hallucination in multimodal large language models (MLLMs), where generated text doesn't match the input image. To solve problems with existing benchmarks, the authors propose LongHalQA, a new benchmark with 6,000 complex hallucinatory texts that mimic real-world scenarios. It introduces two tasks: hallucination discrimination and hallucination completion, which combine both discriminative and generative evaluations into a single multiple-choice format. This approach avoids the need for LLM evaluators, making the evaluation process more reliable and efficient. The paper also presents a new pipeline for creating complex hallucination benchmarks and provides experiments showing that recent MLLMs struggle with long, complex text." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. How to evaluate model with the Hallucination Completion task? What is the prefix text for evaluation? Is it the first word?\n2. Why the Hallucination Completion can be seen as generative evaluation? The multi-choice question still is discriminative question.\n3. “then analyze and filter them based on dataset annotations and GroundingDINO”: how did authors analyze and filter?\n4. The proposed benchmark doesnt \n5. Lack of comprehensive survey of hallucination on Large Vision-Language Models.\n[1] Object hallucination in image captioning\n[2] Halle-switch: Rethinking and controlling object existence hallucinations in large vision language models for detailed caption\n[3] FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models\n[4] Analyzing and mitigating object hallucination in large vision-language models\n[5] FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback\n6. The proposed LLM-free hallucination benchmark does not offer significant advantages, as the approach still requires various tools, LVLMs, and manual verification, leading to low efficiency.\n7. The benchmark has not demonstrated greater reliability compared to existing ones, such as through experimental validation." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose LongHalQA, an LLM-free hallucination benchmark comprising 6.5k long and complex hallucination text well aligned with real-world scenarios, with two MCQ tasks hallucination discrimination and completion for evaluation." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024longhalqa,\ntitle={LongHal{QA}: Long-Context Hallucination Evaluation for MultiModal Large Language Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=514rdneWOX},\nnote={under review}\n}" }, "abstract": { "value": "Hallucination, a phenomenon where multimodal large language models(MLLMs) tend to generate textual responses that are plausible but unaligned with the image, has become one major hurdle in various MLLM-related applications. Several benchmarks have been created to gauge the hallucination levels of MLLMs, by either raising discriminative questions about the existence of objects or introducing LLM evaluators to score the generated text from MLLMs. However, the discriminative data largely involve simple questions that are not aligned with real-world text, while the generative data involve LLM evaluators that are computationally intensive and unstable due to their inherent randomness. We propose LongHalQA, an LLM-free hallucination benchmark that comprises 6K long and complex hallucination text. LongHalQA is featured by GPT4V-generated hallucinatory data that are well aligned with real-world scenarios, including object/image descriptions and multi-round conversations with 14/130 words and 189 words, respectively, on average. It introduces two new tasks, hallucination discrimination and hallucination completion, unifying both discriminative and generative evaluations in a single multiple-choice-question form and leading to more reliable and efficient evaluations without the need for LLM evaluators. Further, we propose an advanced pipeline that greatly facilitates the construction of future hallucination benchmarks with long and complex questions and descriptions. Extensive experiments over multiple recent MLLMs reveal various new challenges when they are handling hallucinations with long and complex textual data." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "hallucination benchmark", "multimodal large language model" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/7a68a8952bd68537c03bd83f2f0c078511d6e908.pdf" }, "presentation": null, "primary_area": { "value": "datasets and benchmarks" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see weaknesses" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The idea of adding learnable noise in different training phases and correction with self-attention to improve generation diversity is novel and interesting.\n\n2. The authors demonstrate improved performance on the IS and FID metrics on the CUB dataset and on the FID metric on the MS-COCO dataset.\n\n3. The authors provide an ablation study demonstrating improvements in results by adding Diversity Injection (DI) and Consistency Enhancement (CE) modules." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work proposes DICE-GAN, a single-stage text-to-image GAN to produce high-quality and high-diversity images with improved semantic consistency with text condition. The paper proposes two modules: The Diversity Injection (DI) module, which adds learnable noise to the image features for increasing diversity in generated images, and the Consistency Enhancement (CE) module, which allows the model to dynamically adjust the weights of different image features according to input text conditions for improved semantic consistency and fidelity." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The novelty of the work is limited. The idea of feature fusion in Eq 1 in the DI module is not novel and has been explored before[1,2,3] in the context of image generation. Further, the idea of masking features in a condition-dependant manner has limited novelty. 2. Lack of clarity in Sec 3.2 writing and Fig 4. The idea behind Conditional Channel Attention mask($M_c$) and Spatial Attention attention($M_s$) is unclear. The motivation behind generating masks from both average and max channels is also unclear. Further, quantities including $G^{c}_{max}$ and $G^{c}_{avg}$ are missing in Fig 4, making it difficult to understand figure pipeline. 3. The authors claim that Dice-GAN utilizes a single-stage model structure for improved performance but are missing comparisons with multi-stage methods, including StackGAN++[4]. 4. Missing ablation studies: - Why are two feature fusion layers are needed in the DI module? How was this hyperparameter determined? - How does learnable noise $\\sigma$ vary when going from lower to higher layers in the trained model? - Missing ablation on design choices in CE module on use of average and max features and conditional channel attention and spatial attention submodule. 5. The proposed method achieves a lower IS score on the MS-COCO dataset, and the authors argue that this is due to the Inception model used in IS computation being pre-trained on the ImageNet dataset. The authors should provide results on Imagenet or Imagenet subset to back their claims.\n\n[1] Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. Film: Visual reasoning with a general conditioning layer. In AAAI, 2018. 2, 5\n[2] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, 2019. 5\n[3] Peebles, William, and Saining Xie. \"Scalable diffusion models with transformers.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n[4] Zhang, Han, et al. \"Stackgan++: Realistic image synthesis with stacked generative adversarial networks.\" IEEE transactions on pattern analysis and machine intelligence 41.8 (2018): 1947-1962." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please, see the weakness." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. Enhanced Diversity: The Diversity Injection module injects noise and text vectors multiple times, ensuring a broad range of image outputs without sacrificing structure.\n\n2. Improved Consistency: The Consistency Enhancement module dynamically adjusts focus on image regions, aligning visuals closely with text descriptions." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this work, they propose the diversity injection and consistency enhancement module for text-to-image generation. This method contribute to produce high-quality images with increased diversity and enhanced semantic consistency based on text descriptions." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. A comparison with recently proposed text-to-image generation models is needed. Not only should there be an analysis of issues with GANs, but also recent Diffusion models, along with performance comparisons. Is there a specific reason you only compared with ShiftDDPMs in the case of Diffusion models? Please provide a detailed response.\n\n2. Please provide a detailed explanation of the table and figure captions.\n\n3. Performance comparisons on diverse datasets are required. Additionally, besides IS and FID, comparisons with other performance metrics are requested (e.g., CLIP score).\n\n4. The examples of qualitative results are too limited.\n\n5. There is a lack of experimental analysis demonstrating the effectiveness of the proposed model structure." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. How does Dice-GAN perform under different levels of input noise? Given the pivotal role of the DI module, understanding the model's sensitivity to noise levels could provide valuable insights into balancing image diversity and visual quality effectively.\n\n2. What measures were implemented to ensure that the DI module does not excessively degrade visual quality due to noise injection? A detailed discussion on the strategies used to balance noise injection and maintain visual quality would be beneficial.\n\n3. Does the CE module exhibit limitations in maintaining semantic consistency for longer, more detailed text descriptions? An analysis of the CE module's performance with nuanced and complex descriptions would provide a clearer understanding of its efficacy in handling diverse linguistic inputs." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The introduction of the DI and CE modules marks a significant advancement in text-to-image synthesis. The DI module, which injects noise at multiple stages of generation, and the CE module, which integrates word vectors with hybrid attention, effectively improve both image diversity and semantic consistency.\n\n2. This method achieves SOTA performance." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The manuscript introduces Dice-GAN which incorporates Diversity Injection and Consistency Enhancement modules to address critical challenges in generating high-quality, diverse images while maintaining semantic alignment with textual descriptions. Experimental results demonstrate that Dice-GAN outperforms state-of-the-art models on the CUB and MS-COCO datasets, underscoring its efficacy in enhancing visual quality and fidelity." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The manuscript lacks a detailed examination of the model's performance across varying levels of text complexity. Given that text descriptions can range from simple to highly nuanced, an analysis based on text complexity would provide stronger evidence of the model's robustness and its ability to handle diverse linguistic inputs.\n\n2. The reviewer wants to see the experiment about computational efficiency.\n\n3. The study does not thoroughly investigate the model's capacity to handle various textual attributes, such as color, size, and object positioning. A more focused evaluation of these specific attributes could offer deeper insights into the model's capability to accurately reflect detailed descriptive features and further demonstrate its adaptability." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see weakness." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- Clear and well-organized presentation.\n- Superior performance to other GAN-based methods." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "he paper proposes Dice-GAN, an efficient attention-based text-to-image synthesis model. To enhance image diversity, a diversity injection module is introduced, incorporating noise and a self-attention mechanism. A consistency enhancement module, combining word vectors and a hybrid attention mechanism, improves semantic consistency. Experimental results on CUB and COCO datasets demonstrate Dice-GAN's superiority in image fidelity and diversity compared to existing approaches." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Limited novelty: While the diversity injection module is a contribution, the core idea of adding noise is not entirely novel.\n- Lack of comparison to diffusion models: Given the current dominance of diffusion models in text-to-image generation, a more comprehensive comparison to state-of-the-art diffusion-based methods is essential to establish Dice-GAN's significance.\n- Insufficient discussion of other generative models: The paper could benefit from a more in-depth discussion of how other generative models, such as flow-based models and StyleGAN, could be adapted or combined with Dice-GAN to further enhance diversity and quality." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "This paper propose a novel GAN-based model called Dice-GAN with diversity injection and consistency enhancement." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024dicegan,\ntitle={Dice-{GAN}: Generative Adversarial Network with Diversity Injection and Consistency Enhancement},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=5187wrocJq},\nnote={under review}\n}" }, "abstract": { "value": "In the field of natural language description tasks, one challenge for text-to-image modeling is to generate images that are both of high quality and diversity and maintain a high degree of semantic consistency with the textual description. Although significant progress has been made in existing research, there is still potential for improving image quality and diversity. In this study, we propose an efficient attention-based text-to-image synthesis model based on generative adversarial networks named Dice-GAN. To enhance the diversity of image generation, we design a diversity injection module, which injects noise multiple times during image generation and incorporates a self-attention mechanism to assist the generator in maintaining global structural consistency while enhancing the diversity of images. To improve the semantic consistency, we designed a consistency enhancement module, which enhances the semantic consistency of image generation by combining word vectors and a hybrid attention mechanism to achieve dynamic weight adjustment for different image regions. We conducted experiments on two widely accepted benchmark datasets, CUB and COCO. Dice-GAN demonstrated significant superiority in improving the fidelity and diversity of image generation compared to the existing approaches." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "text-to-image", "generative adversarial networks", "self-attention", "semantic consistency" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/3be2b3f54f85aa65260c99a6858191ea851af94d.pdf" }, "presentation": null, "primary_area": { "value": "generative models" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Dice-GAN: Generative Adversarial Network with Diversity Injection and Consistency Enhancement" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1) The term $\\alpha$, which is used extensively in formulation of metrics and overall thorough the paper, is only loosely defined in appendix. While it may be the usual practice in math-heavy papers, it does substantially confuse readers who are not so proficient. It is quite pity to read a definition or proof and find terms that simply not defined anywhere above. Consider defining $\\alpha$ in the main text of the paper.\n2) How increased $\\lambda_r$ impacts metrics other than diversity?\n3) How does proposed EO objective impacts training efficiency (in terms of increased latency or increased VRAM requirements)? Does it limit it's applicability?" }, "rating": { "value": 10 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1) Authors provide 4 carefully defined metrics, along with necessary guarantee proofs. Overall, the paper is very well composed.\n2) Those 4 proposed metrics allow to comprehensively evaluate model unlearning using entire output distribution (potentially, right now it is limited to MC sampling on certain examples). This appears to be a novel contribution and addresses the lack of probablistic evaluation in the field of unlearning.\n3) This approach can be potentially extended to other tasks which require reliable evaluations.\n4) The proposed entropy optimization objective is clearly defined and is formuated as additive terms which can be applied to existing unlearning losses, which makes it easy to implement. Addressing diversity on retain samples allows to ensure that models remains useful after unlearning." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper authors address the problem of reliable unlearning in LLMs. First they introduce a problem, that evaluations based on deterministic point estimates (sampled texts) fail to reliably catch the risks exposed in probablistic outputs. For the case of unlearning, authors state that existing methods rely on a single generated sequence to identify if the information leakage is present or not. Which might not be enough when assessed model might still eventually produce a text with leaked information (with some probability). Therefore authors propose a set of 4 metrics aiming accurately quantify information leakage in model output distribution. Then, authors propose a novel unlearning training objective, which aims to simultaneously minimize model's output distribution entropy on a set of \"forget samples\" while retaining diversity on \"retain samples\". The loss itself is a set of additional terms which can be applied to some existing unlearning objectives. Finally, authors conduct comprehensive evaluation of unlearning with different methods using the proposed metrics." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1) While paper title reads as \"A Probabilistic Perspective on Unlearning and **Alignment** for Large Language Models\", authors effectively **do not touch** the alignment in their work, leaving it for further research. Indeed, alignment is only mentioned in Introduction, Limitations and Conclusion. This paper would benefit from having at least small discussion on how the proposed metrics can be extended to other evaluation tasks." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- L118: $V^\\infty$: I believe that the more accepted notation for sequences of arbitrary sequences is $V^*$, where $*$ is the Kleene star. \n - L150: \"extend\" -> \"extent\".\n - L177: \"Binary case\": This exposition feels a bit verbose. My understanding is that you are empirically fitting a Beta distribution based on whether data is leaked through your Monte-Carlo experiments, and outputting a quantile based on a desired safety level $\\alpha$. Please correct me if my understanding is not correct.\n - L197: Make this part more self-contained: especially, what is the Dvoretzky-Kiefer-Wolfowitz inequality and how does it apply here?\n - L211: Proposition 3: This lower and upper bound is reminiscent of the Darboux integrals of $F_n$. If possible, please elaborate on the relationship of this bound estimate to an underlying integral expression. Additionally, it'd be good to reiterate that $F_n$ is the empirical CDF.\n - L225: What does $\\eta_i$ bound? Please discuss.\n - L230: $M_4$: I think it'd be better to call this something like $M_\\sigma$, to be clear that this is not an estimate of the probability.\n - L246: $\\bar X + 2\\bar \\sigma$: why 2? I believe this is a choice based on the safety level, but it would be better to define this as a hyperparameter whose selection is based on the accepted risk level." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The paper proposes metrics that is defined on the output distribution rather than the point estimate. I think this is a remarkable step and should be considered by various other scenarios.\n - The exposition on the estimation of probability of leakage and bounds of standard deviation are intuitive and sound. \n - The set of experiments presented are convincing." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposed a set of metrics that bounds the risk of unlearning by estimating the bounds of probability of leaks, and the deviation of such random variables. Instead of computing the metric over deterministic point estimates drawn from greedy decoding of LLMs, it proposes the use of Monte Carlo methods, and then estimates these bounds by computation over the empirical distribution. Additionally, the authors proposed some mitigation methods to reduce the risk of leakage when fine-tuning a LLM, and show through experiments that such measures offer potential for reducing the risk and undesired biases in model outputs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Notations can be improved in the exposition. For example, $M_1, \\cdots, M_4$ actually stands for estimates of different variables, rather than 4 different ways of estimating the same variable. See questions below for more suggestions. \n - Some derivation are not self-contained (e.g. in Metric 2, the $\\epsilon = \\sqrt{\\frac{\\log (1/\\alpha)}{2n}}$ is not self-contained and is derived from prior work. \n - Expositions tend to be a bit too formal, and lacking some intuitions and insights. See below." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "See weaknesses." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. **Novel Perspective on LLMs Unlearning Evaluation** Existing deterministic metrics are apparently insufficient for LLM unlearning evaluation, and this paper introduces a probabilistic perspective to mitigate this issue.\n\n2. **Adequate Mathematical Derivation** In this paper, the authors demonstrate the rationality of their method theoretically and empirically." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a probabilistic perspective on LLM evaluation which shifts from single point estimates towards evaluating entire output distributions offers significant potential for the field of unlearning and proposes a novel framework to directly assess the output distribution of a model. Besides, an unlearning loss based on entropy optimization and adaptive temperature scaling is also proposed." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. **More Discussion on LLM Alignment Evaluation** Since the titile contains \"ALIGNMENT\", more discussion on this topic should be included in this paper." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- For entropy optimization, I'm not sure about the intuition to minimize the entropy on Dfg. Wouldn't this lead the model to be confident on a different answer, which I think might be a strange thing to enforce.\n- It would be interesting to how unlearning (as well as the proposed optimization methods) affects the model's general ability." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Designing good evaluation metrics is important for a reserach direction such as unlearning, and this work indicates a limitation of existing metric and correspondingly proposes improved metrics.\n- The proposed metrics and methods are shown to be effective for two recent unlearning benchmarks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work introduces a probablistic perspective for LLM unlearning evaluation. Instead of relying on deterministic greedy decoding in existing evaluation methods, this work takes a probablistic framework and derive metrics considering the high-probable output distributions. The proposed metric demonstrates the limitations of previous methods for their lack of identifying false unlearning. Moreover, a novel loss based on entropy optimization and adaptive temperature scaling are introduced to improve model unlearning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- There is a lack of algorithmic description on how the proposed metrics are calculated, without which readers who lack certain statistical machine learning knowledge or who want to implement the metrics would find it difficult to understand and apply the proposed metrics.\n- The proposed metrics are only tested for the unlearning case, which surely is indeed a well-suited scenario. Nevertheless, it would nice if it can be extended to more use cases, such factuality, to verify the effectiveness of the metrics." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We demonstrate that existing deterministic evaluations in large language models are insufficient and propose a novel probabilistic evaluation framework that considers the whole output distribution of a model." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024a,\ntitle={A Probabilistic Perspective on Unlearning and Alignment for Large Language Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=51WraMid8K},\nnote={under review}\n}" }, "abstract": { "value": "Comprehensive evaluation of Large Language Models (LLMs) is an open research problem. Existing evaluations rely on deterministic point estimates generated via greedy decoding. However, we find that deterministic evaluations fail to capture the whole output distribution of a model, yielding inaccurate estimations of model capabilities. This is particularly problematic in critical contexts such as unlearning and alignment, where precise model evaluations are crucial. To remedy this, we introduce the first formal probabilistic evaluation framework in LLMs. Namely, we derive novel metrics with high-probability guarantees concerning the output distribution of a model. Our metrics are application-independent and allow practitioners to make more reliable estimates about model capabilities before deployment. Through a case study focused on unlearning, we reveal that deterministic evaluations falsely indicate successful unlearning, whereas our probabilistic evaluations demonstrate that most if not all of the supposedly unlearned information remains accessible in these models. Additionally, we propose a novel unlearning loss based on entropy optimization and adaptive temperature scaling, which significantly improves unlearning in probabilistic settings on recent benchmarks. Our proposed shift from point estimates to probabilistic evaluations of output distributions represents an important step toward comprehensive evaluations of LLMs." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Machine Unlearning", "Alignment", "Large Language Models" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/76e657968639e5b60f05d2f112c07c2c586cd7c2.pdf" }, "presentation": null, "primary_area": { "value": "alignment, fairness, safety, privacy, and societal considerations" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/0f9019f956a111199ce1427499e912b8acedd0cf.zip" }, "title": { "value": "A Probabilistic Perspective on Unlearning and Alignment for Large Language Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1) Why chatgpt 3.5? Could you justify the choice of this model? Why was chatgpt 3.5 the model chosen for comparison, is it a reasonable choice for a baseline? \n\n2) Could you generally talk about the distribution of the Likert scale that you got from the pairwise evals? Was there anything at all in which chatgpt was substantially better and generally chosen? (Assumption here that I suppose llama-2 would be usually better than chatgpt 3.5 in all cases)\n\n3) if these outputs were obtained from Chatgpt 3.5, which API was it received from, and what was the exact cutoff (e.g., ChatGPT-3.5-0604, etc.)?\n\n4) Pairwise evals ultimately show revealed preferences and model choice between two outputs. Do you think this translates to human evaluation directly on model outputs (not comparisons) on NLP parameters like coherence, semantic relevance, factual relevance, etc.? Could you comment on the choice of pairwise evals?\n\n5) Just a general question about related work: is there no related work? (while this correlation aspect might not have been explicitly studied), Studies have considered which of them is better in MT, summarization, and other NLP areas. Can you provide a more comprehensive overview of related work, including studies that have compared human evaluations and benchmarks in specific NLP tasks like MT/Summarization, and contextualize this work in the broader field?\n\n6) Could you conduct a detailed analysis of features/characteristics shared by highly correlated benchmarks? I think that would help a lot in designing benchmarks in the future." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1] The question at the center of the paper -- \"Correlation between NLP benchmarks and Human Evaluations,\" is an important central question to NLP evaluation in general. Human Evaluations are considered (somewhat so) the gold standard of evaluation but are extremely time-consuming and expensive to run; as models get more capable the human evaluations also get even more costlier because now we require experts to evaluate vs requiring less advanced folks earlier, but we can reliably construct more difficult benchmarks for models, so if these two things are correlated, perhaps lesser focus can be placed on human evaluations. \n\n2] Predicting Human Evaluations is a difficult task, and LLMs as judges are being increasingly explored as an alternative to human evaluations. The method in the paper also showcases some important insights into this process." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper initially explores the correlation between NLP benchmarks and human evaluation. With the advent of increasingly capable LLMs, human evaluations have become a steady and major alternative choice to evaluate the efficacy, performance and capabilities of LLMs. An important question that generally arises with the choice is whether NLP benchmarks are useless since human evaluations are costly and time consuming and are not always a gold standard. Where do NLP benchmarks fall? This paper explores this question and also explores the possibility of predicting human evaluations from NLP benchmarks. \n\nTwo key questions are asked:\n- To what extent are human evaluations and NLP benchmarks correlated?\n- How well can benchmarks predict expensive and time-intensive human evaluations?\n\nThe researchers use all the Llama chat-2 models (7,13,30 and 70B parameters) to establish this, which were trained on 2T tokens and fine-tuned using SFT and RLHF. Human evaluations are collected by evaluating the Llama2 chat models pairwise against ChatGPT 3.5 on dataset a of single-turn and multi-turn prompts, where responses are sampled from each model. 3 Human annotators independently provide a pairwise comparison on the Likert scale (1 to 7, where 1 means chat llama preferred and 7 means chatgpt 3.5 preferred). uThey end up doing a large-scale study spanning factual questions, language assistance, writing, procedural questions, reasoning and many more. The Chat Llama 2 models are evaluated on many popular NLP benchmarks right from AGI Eval, Ai2 Reasoning Challenge, Big Bench Hard, Boolq, commonseqa, GSM8k, MMLU, MATH, QuAC, PiQA and many more. Standard evaluation processes are used. \n\nThe findings revealed that NLP benchmarks are broadly highly correlated with human evaluations, with certain benchmarks showing particularly strong correlations. The most predictive benchmarks included specific subsets of MMLU (covering topics like nutrition, human aging, and sociology), portions of BIG Bench Hard, HellaSwag, ARC, RACE, PIQA, Natural Questions, QuAC, and CommonSenseQA. However, some benchmarks showed weaker correlations, including ETHOS, Kth Sentence, most of Inverse Scaling, OpenBookQA, COPA, SciBench, and SIQA.\n\nUsing overparameterized linear regression, the researchers successfully demonstrated that NLP benchmark scores could predict human evaluation scores with reasonable accuracy. Despite the small sample size of only four models, leave-one-out cross-validation showed promising results, suggesting that faster and cheaper NLP benchmarks might effectively predict slower, more expensive human evaluations in many cases.\n\nThe authors note several limitations, including the small sample size, the assumption of linearity in their predictive models, and potential limits to generalizability across different model families thus rounding up the study and paving the way for future work as well." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1] The small sample size brings into question the generalizability of these insights and results.\n\n2] Only uses GPT-3.5 as the comparative model, no insight is provided into why this is the case? And also lacks any discussion of whether chatgpt 3.5 is a reasonable choice of a baseline. \n\n3] Perhaps a granular analysis of what makes a benchmark more correlated? Is there something common in the correlated benchmarks? This would also pave the way to designing and determining better benchmarks." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "- More LLMs should be covered in this study. I understand the computational cost during inference and the cost in human evaluation, but four LLMs are definitely too few to support subsequent experiments.\n- I do need more details of the human evaluation in your study. What makes me most confused is the selection of the prompts. Why don't you use the same question sets as those of automated NLP benchmarks? If there are too many, you can sample from each dataset. Now there is a mismatch between the prompts (questions) in human evaluation and automated NLP benchmarks and the mapping relationship is not clear. Even if ignoring the mismatch issue, you should provide the number of prompts per area and categories used in human evaluation.\n- The experiments of one-rank decomposition in Section 3.3 need to be further explained. Can you better state your motivation of conducting this decomposition and what insights can we draw from that?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The research question of this paper—the relationship between evaluation results from automated NLP benchmarks and human evaluations —is generally important and meaningful. Recently, numerous automated benchmarks and human evaluations have emerged separately, but there has been little research on the relationship between them.\n- This paper covers many automated NLP benchmarks and includes a large-scale human evaluation, which lends a certain level of generality to its results." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper studies the relationships between the evaluation results of automated NLP benchmarks and those of human evaluation. It mainly revolves around two research questions: how well human evaluations and NLP benchmarks are correlated with each other, how well NLP benchmarks can predict human evaluation. Specifically, the authors develop a set of 1917 prompts organized by areas, categories, and subcategories, selects four LLMs from Llama 2 family, gets their reponses to the prompts, and conducts a large-scale pairwise human evaluation. The evaluation results of the four models on many automated NLP benchmarks are also derived. Then, the paper analyzes the correlations between human evaluation and automated NLP benchmarks and finds that they are highly correlated in most cases. Furthermore, the authors decompose the correlation matrix into rank-one components and demonstrate the communities between human evaluations and NLP benchmarks. Finally, the authors tried to fit a regression model to predict the human evaluations with automatic evaluation results as inputs." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although the idea of this paper is beneficial, many obvious flaws diminish its value.\n\n- This study uses only four LLMs, which is too few. This leads to\n - The correlations between automated NLP benchmarks and human evaluation are calculated merely from two four-dimension vectors, which is unreliable \n - Insufficient experiments for predicting human evaluation from automated NLP benchmarks, despite cross-validation conducted in the paper\n\n- The paper lacks key details, including but not limited to how the prompt set used in human evaluation is obtained, the human evaluation process and its reliability (e.g. inter-annotator agreements), details of how the correlation is calculated (what is ~150 evaluation process?), the settings for linear regression. This not only creates difficulty in understanding but also raises doubt about the rigor of this study.\n\n- The presentation of the paper could be improved. For instance, the font sizes in Fig 3, the upper part of Fig 4, and Fig 6 are too small, making it hard to read." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please refer to Weaknesses." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The motivation and research questions of this work are very interesting and significant. Considering that language models are becoming increasingly powerful, many traditional NLP benchmarks may have lost their discriminative power, leading researchers to turn to human evaluations, which are more costly and harder to reproduce. By analyzing the consistency between NLP automatic evaluation benchmarks and human evaluations, this work aims to identify highly consistent benchmarks to simulate human evaluations, thereby reducing evaluation costs. Their experiments cover a large range of datasets and settings, including constructed various categories of human evaluation data and many common NLP automatic evaluation benchmarks, demonstrating a very comprehensive effort." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work attempts to explore the correlation or consistency between common NLP automatic evaluation benchmarks and human evaluations in analyzing and comparing the capabilities of language models. They cover a wide range of datasets and conduct experiments on four different sizes of Llama 2 models and GPT-3.5, employing human annotators to provide evaluation data. They find that there is a high correlation between automatic benchmarks and human evaluations, and they identify which benchmarks show stronger correlations. Furthermore, they also fit models to predict human evaluation scores of language models from academic evaluation scores." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Although the research topic of this work is meaningful, it is also actually very complicated and corresponds to a more challenging analysis process. Even though the work has tried to handle the experimental data and present corresponding results as macroscopically as possible, their experimental analyses remain confusing and fail to help readers capture the main points. From Figure 1 onward, the clarity and readability of the charts decline rapidly, and by Figure 6, it becomes nearly impossible to extract any information as the fonts are extremely small and the visualized results are poorly presented.\n\nSome analytical settings in the paper are unclear or somewhat unreasonable. For example, in line 149, what does the \"evaluation process\" refer to, and why are approximately 150 combinations calculated in total? What do they represent? Additionally, if I understand correctly, it seems unfair to compare human evaluation results across mixed task types with different NLP automatic evaluation benchmarks that may focus on testing certain different abilities." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "- Q1. How do the authors conduct the experiment using the Llama-2-30b model? In fact, there is no 30b model in the LLama2 series, and I assume the authors are referring to the Llama-2-34b model. However, even Llama-2-34b-chat (or the base model) is not officially released, so I wonder how this paper conduct experiments using Llama-2-34b-chat." }, "rating": { "value": 1 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 1 }, "strengths": { "value": "- This paper studies a very important problem: whether scores on NLP benchmarks correlate with human evaluation results. This can potentially guide researchers to construct better benchmarks\n- This paper studies the possibility of using NLP benchmarks to predict human evaluation results. Considering the efforts of human evaluation, the problem studied in this paper can help us develop LLMs faster." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the relationship between NLP benchmarks and human evaluation results and aims to understand what roles NLP benchmarks should play in the era of LLM. They conduct human evaluations on four Llama 2 chat models and calculate the correlation between human evaluation results NLP benchmarks, spanning from open-domain QA, MMLU, and safety/adversarial datasets. They find that most NLP benchmarks correlate well with human evaluation results, and it is possible to predict human evaluation results based on scores on NLP benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The experiment parts are highly unclear and hard to comprehend. It is unclear how the correlations are calculated between human evaluation and NLP benchmark scores. There is even no **Experiment Setup** section in this paper, and the part that most looks like the experiment setting is the first seven lines of Section 3. After repeatedly reading those lines, I still cannot understand how the correlations are calculated. Precisely,\n - How do you aggregate the scores of different shots?\n - Why do you aggregate the results of different shots?\n - What is the number of shots?\n - How is the prompt formatted?\n - How are the demonstrations in the few-shot selected?\n - Where does the number *150* on Line 148 (page 3) come from?\n - How is the human evaluation conducted? How many samples are there in the single-turn and multi-turn dialogues? How are the topics selected? What is the distribution of the data?\n - If the paper only uses four models, is the correlation coefficient calculated using only the benchmark scores of 4 models and the human evaluation results of the models? This means we are only calculating the correlation coefficient between two sets for numbers with only four elements in each set. \n\n- There are only four models used in this paper: the four chat models in Llama-2 with different numbers of parameters. The abilities of those models are very distinct, so it is easier for human evaluators or NLP benchmarks to distinguish the strengths of these models. A more challenging and realistic scenario is to consider more LLMs whose abilities are more diverse.\n\n- The figures in the paper are terribly and poorly formatted. Those figures do not seem like they are designed to be read. The font sizes in the figures are too small to read and clustered together. I need to zoom in to 400% on my computer to see the words.\n\n- Section 3.3 is highly unclear, without explaining what the *communities* this section is discussing and with no experiment settings that allow the readers to understand what is happening now. \n\nConsidering that the experiment setting is highly unclear and the results are poorly presented, it is impossible to evaluate the contribution of this work. The paper requires major refinement. However, the paper studies an important problem, and I encourage the authors to keep working on this topic." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We compare human evaluations and academic evaluations of language models against one another" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024correlating,\ntitle={Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=52Idqv2FNY},\nnote={under review}\n}" }, "abstract": { "value": "The field of natural language processing (NLP) historically evaluated language models using benchmarks with automated metrics. However, the recent advent of highly capable chat language models (LMs) has caused a tectonic shift from NLP benchmarks to human evaluations. The relationship between these two evaluation processes is unclear and underexplored for chat LMs. Broadly, to what extent are human evaluations and NLP benchmarks correlated with one another? How well can computationally inexpensive and automated benchmarks predict expensive and time-intensive human evaluations? Which benchmarks provide predictive signals for human preference for LMs? What role, if any, should benchmarks play in the era of chat LMs? To answer these questions, we conducted a large-scale study of the relationships between human evaluations and benchmarks. We show that benchmarks are broadly highly correlated with human evaluations, and we identify which benchmarks exhibit strong correlations with human evaluations and which do not. Having established that reliable correlations exist, we fit models to predict a language model's human evaluation scores from its academic evaluation scores and provide evidence that such predictive models can generalize across LM scales." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "language models", "evaluations", "human evaluations", "benchmarks", "NLP benchmarks" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/afcddce2632006a71cf976e3ad0a2c20cc185756.pdf" }, "presentation": null, "primary_area": { "value": "datasets and benchmarks" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- Authors suggest that they use a sigmoid instead of an indicator function to make the DLGN differentiable for training. Have authors considered using a temperature parameter for the sigmoid (potentially annealed during training), as common in other continuous relaxation methods?\n- On lines 406-407 authors suggest that they use DBScan for clustering hyperplanes due to this algorithm being “robust to outliers” — why do authors expect outliers to be a significant issue here?\n- How do authors explain the observation that certain layers of the DLGN are more prone to matching the true discontinuities than others (Figure 2)?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- Originality. The DLGN is an interesting architecture that combines deep linear networks with the gating mechanism to construct a novel class of non-linear models. One could also treat DLGN as a novel decision tree parametrization which, when relaxed using a sigmoid, can be learned by back-propagation. To the best of my knowledge, DLGN is a novel, original model architecture, though its connection to soft decision trees should be studied more carefully." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a novel model architecture (deep linearly gated network, DLGN) and studies the way in which this model seeks out “label discontinuities” in the data during training. This analysis is enabled the fact that we can enumerate such label discontinuities in a DLGN. The synthetic datasets with known discontinuities are generated by another model — an oblique decision tree (ODT). The authors also show how an ODT can be constructed from a trained DLGN, for the purpose of interpretability. Finally, the paper presents results of fitting a DLGN to several UCI regression tasks, comparing performance to several tree-based, kernel-based, and NN-based baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- Lack of focus. At the moment, the paper’s focus is split between a study of feature learning using DLGN (chapter 5), and a study of the DLGN itself as an expressive, yet interpretable model architecture (chapter 6). To me these are two orthogonal contributions, and the paper would be stronger if authors focused on one of these.\n- Significance. The significance of the proposed model and the feature learning study presented in the paper is not clear to me. \n 1. Capturing “label discontinuities” (are these not simply decision boundaries?) is at the core of solving a classification problem, hence it is not surprising that a model which works well on the task has to discover such hyperplanes — I don’t see an alternative way that a model can solve a task. The real question is how an over-parametrized model can correctly identify high-dimensional decision boundaries given limited data — this is the main mystery in the theory of deep learning at the moment, and one that this paper doesn’t shed much light on.\n 2. To understand the significance of the findings for deep learning, we would need to understand the relationship between a DLGN and a DNN. While it is nice (albeit, not surprising) to see how a DLGN uncovers the true “label discontinuities”, how can we know that a DNN will demonstrate the same behavior?\n 3. While the proposed architecture is appealing due to potentially being both expressive and interpretable, results on the real (UCI) datasets suggests that DLGN is comparable in performance to standard tree algorithms. There is little evidence that we should prefer the proposed architecture to e.g. the well-studied random forests, which we could also argue to be “interpretable”. (I do not consider results on synthetic data to be good evidence, given the connection between ODTs used to generate the data and DLGN.)\n 4. While the authors claim that the proposed architecture is interpretable, no interpretations of the models fit to real data are given. If interpretability if the main selling point of the architecture, I would expect a deeper analysis focused on interpretability.\n- Presentation. Please consider moving the DLGN model diagram to the main text: it’s difficult to understand the model architecture from the formulas alone. Also, please try to stick to academic language, and avoid informal phrases like “well nigh impossible”, “handily outperform”, “succeed comfortably”, “about the same”, etc." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "1. What's the purpose of defining the manifold $\\mathcal{M}$ in line 133?\n2. In line 239, Equation (4), is there a missing transpose on $\\mathbf{u}_{i_1}^1$?\n3. I find it difficult to understand how the computational cost of a forward pass for Equation (1) is less than twice that of a ReLU network with $mL$ nodes. Could the authors provide further clarification on this?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "1. Clear writing. The authors study a specific class of problems with great clarity. \n2. The authors' persistence in tackling a challenging yet manageable problem setting is commendable.\n3. Like a black-box learner, the DLGN is able to learn non-linear features. Yet, it still provides mechanistic interpretability.\n4. DLGN outperforms both tree-based and non-tree algorithms, as well as ReLU networks, in the oblique decision tree setting, while maintaining strong competitiveness on real-world tabular datasets.\n5. The authors provide a framework that paves the way for future research and development." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors introduce a model called the Deep Linearly Gated Network (DLGN) to study feature learning, specifically in binary classification tasks defined by an oblique decision tree labeling function. They use DLGN to test the hypothesis that during training, the model’s discontinuities move towards label function's discontinuities. The paper includes evaluations on dozens of open tabular datasets to compare DLGN with ReLU networks and tree-learning algorithms." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I'm not seeing effective weaknesses." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "See the weakness part." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "This paper is a bold attempt to deepen our understanding of feature learning in deep learning. The hypothesis, data setup, network architecture, and approach to interpretability are unconventional, and they bring a fresh perspective to the literature. This work has the potential to inspire new ideas and serve as a valuable starting point for further exploration." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors propose a mechanism to intuitively explain why neural networks can surpass kernel methods through feature learning. They hypothesize that the feature learning process involves the alignment of model function discontinuities with label function discontinuities during training. To explore this, they introduce a new network architecture called the Deep Linearly Gated Network (DLGN), designed as a surrogate for ReLU networks. They argue that this architecture retains similarities to ReLU networks while offering easier interpretability. Under this framework, they provide empirical evidence showing how model function discontinuities move toward label function discontinuities during training, facilitated by feature learning." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "From my perspective, while this work is intriguing, it is not yet ready for formal publication. My concerns are as follows:\n\n1. The authors reference previous studies examining the dynamics of single hidden layer models under specialized data and settings to push beyond kernel methods or deep linear models (Damian et al., 2022; Ba et al., 2022). They appropriately note that these analyses, often focused on specific data settings like the parity function, fall short of addressing the needs or behaviors of deeper networks. However, numerous works also investigate complex feature learning with deep neural networks, such as https://arxiv.org/abs/2305.06986 and https://arxiv.org/pdf/2311.13774. These studies should be acknowledged and compared with the current work.\n\n2. I am skeptical about the extent to which the new architecture resembles a ReLU network. Additionally, it is unclear how this intuition or design could extend to CNNs or transformers. Besides, the current version only applies to binary classification, whereas a universal feature learning mechanism should ideally apply across setups, such as binary classification, multi-class classification, and regression. I am unsure how the proposed intuition extends to these broader contexts. For example, existing papers (such as the two mentioned above) demonstrate that neural networks can efficiently learn $h = g \\circ p$ with $p$ quadratic and $g$ nonlinear via feature learning. How would this intuition explain such cases?\n\n3. In Section 3, the introduction to ODT progresses too quickly. The paper would benefit from a more mathematically detailed introduction to the new concepts presented in this section." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1) Why do you even introduce a manifold $M$ in your notation in line 133? It is never used \n2) On line 192, I don't think I agree that \"no other hyperplane other than the internal nodes has this property\". e.g. consider any other hyperplane that is not one of the internal nodes in the plot in Figure 1; doesn't it also have points of both labels on either side of it?\n3) You introduce this formal notion of $\\gamma(R, f)$ as the local discontinuity coefficient in Section 3, but then never mention it anywhere later in the paper. How would you explain the results in say Figure 2 and 3 in the context of this quantity? Could you elaborate on this a bit?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The proposed theory is intriguing and indeed very interesting. The plots in Figures 2 and 3 are indeed striking---they illustrate how faithfully the DLGN neuron activations are increasingly aligning with the ODT hyperplanes over the process of training. These observations support the theory put forth by the authors about neural network architectures possibly picking up on the discontinuities in the labelling function. The clustering procedure to extract a decision tree is also very interesting, and empirically seems to work well (as suggested by Figure 3). Overall, I find the theory proposed by the authors, along with the striking empirical illustrations, very interesting. These could motivate further theoretical investigations for such phenomena." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a theory to explain how feature learning happens in neural networks. The authors posit that neural networks align (or rather, get attracted to) the discontinuites in the way the target label changes over the input domain. As a pilot test for this theory, the authors consider a setting where the target function to learn is an Oblique Decision Tree (ODT). ODTs correspond to decision trees where each internal node is a linear threshold. Thus, the hyperplanes associated to the nodes in the decision tree split up the input space into different labeled regions (see Figure 1). The hyperplane corresponding to the root is \"most discontinuous\" in terms of how the label changes on both sides of it (owing to further and further splits by its many descendants). The authors posit that the training of non-linear neural networks procedurally aligns the model with these discontinuities.\n\nAs a tractable model to further empirically study this theory, the authors propose a novel neural network architecture which they term Deep Linearly Gated Networks (DLGNs). DLGNs are somewhere in between standard nonlinear (e.g., ReLU activated) deep networks and deep linear networks. The function computed by a DLGN can be writen as a large summation of terms of the form $f_\\pi \\cdot g_\\pi$. Here, $f_\\pi$ is a product of the signs of activations of neurons in a deep linear network (a single neuron from each layer). $g_\\pi$ is a product of the activations themselves (a slight detail is that the weight matrices producing the activations in $f_\\pi$ and $g_\\pi$ are different). Thus, we can think of $f_\\pi$ as an indicator of the intersection of halfspaces, while $g_\\pi$ is the weight we add up if the indicator turns out to be true.\n\nThe authors fix the target function to be an ODT, and train the DLGN architecture on synthetic data labelled by the ODT. The suprising observation is given in Figure 2. At the end of training, if one plots the hyperplanes corresponding to the linear threshold at every neuron, one observes that the hyperplanes (at least in the later layers of the DLGN) align very well with the hyperplane splits in the ODT! Furthermore, Table 1 shows that most of these linear thresholds align with some hyperplane in the ODT. That is, the neurons in the DLGN architecture are \"getting attracted\" to aligning with some internal node in the ODT.\n\nThe authors next use this empirical observation to extract a decision tree out of a trained DLGN. Namely, they plot the linear thresholds corresopnding to all the activations after training, and then cluster these thresholds. The center of the largest cluster is chosen to be a root node hyperplane, and then we recurse this procedure on data on either side of this hyperplane. This procedure is pictorally well illustrated in Figure 3. Again, one can see that on performing such clustering-based decision tree generation, the first cluster center aligns very well with the root node hyperplane, and the phenomenon continues as we recurse. The upshot is that one can extract an interpretable model from the DLGN..\n\nFinally, the authors perform experiments on real-world classification data to illustrate that the classification accuracy of DLGNs is somewhere in between standard ReLU networks and other simpler non-neural network algorithms. Thus, DLGNs have the added benefit of being interpretable, but more powerful than stanard decision trees." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The pilot experiments done by the authors are admittedly specialized. Namely, they don't actually consider standard ReLU networks, but only the DLGNs they propose. Furthermore, they only consider cases where the target function is conveniently an ODT. While this is totally okay as an initial starting point, it does raise the question about whether such empirical phenomena of the neurons aligning with the discontinuities also arise in cases where the target function has discontinuities of a different nature (like curvy discontinuities, etc). But this is not a significant weakness, as it seems beyond the scope of a pilot study. But it would be really interesting to visualize the activations of the neurons (say with quadratic thresholds) for when the target function is also composed of curvy discontinuities." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Deep neural networks outperform kernel machines by learning features through discontinuities in label functions during gradient descent training, showing better performance and offering greater interpretability compared to ReLU networks." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024deep,\ntitle={Deep Networks Learn Features From Local Discontinuities in the Label Function},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=52UtL8uA35},\nnote={under review}\n}" }, "abstract": { "value": "Deep neural networks outperform kernel machines on several datasets due to feature learning that happens during gradient descent training. In this paper, we analyze the mechanism through which feature learning happens and use a notion of features that corresponds to discontinuities in the true label function. We hypothesize that the core feature learning mechanism is label function discontinuities attracting model function discontinuities during training. To test this hypothesis, we perform experiments on classification data where the true label function is given by an oblique decision tree. This setup allows easy enumeration of label function discontinuities, while still remaining intractable for static kernel/linear methods. We then design/construct a novel deep architecture called a Deep Linearly Gated Network (DLGN), whose discontinuities in the input space can be easily enumerated. In this setup, we provide supporting evidence demonstrating the movement of model function discontinuities towards the label function discontinuities during training. The easy enumerability of discontinuities in the DLGN also enables greater mechanistic interpretability. We demonstrate this by extracting the parameters of a high-accuracy decision tree from the parameters of a DLGN. We also show that the DLGN is competitive with ReLU networks and other tree-learning algorithms on several real-world tabular datasets." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Deep Learning", "Feature learning", "Interpretable", "Local Discontinuities", "Deep learning theory", "Deep neural architectures", "Supervised learning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/c93c3a722c5f00913c7a9dd3e7e4d65ee4199ca5.pdf" }, "presentation": null, "primary_area": { "value": "interpretability and explainable AI" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/3af52b256e10d9af378b88cd84a90a9c7265042e.zip" }, "title": { "value": "Deep Networks Learn Features From Local Discontinuities in the Label Function" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Please see weaknesses 1-3." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "(1) Enhances understanding of inductive biases in SSMs for ICL tasks.\n\n(2) Bridges key domains such as SSMs, ICL, and mechanistic interpretability.\n\n(3) Demonstrates generality by extending results to multi-step cases, multiple layers, and multi-dimensional data.\n\n(4) Empirical analysis shows practical alignment with the theory-based construction in simple cases.\n\n(5) Clarity: While some aspects could be improved (such as adding a figure to illustrate the main ideas in Section 3.1), the paper is clear, well-motivated, and easy to follow. It starts with simple cases and provides clear definitions, making it an enjoyable read!" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper investigates the ability of SSMs to perform ICL through gradient descent during the forward pass over recurrent steps. It provides a theoretical analysis of various ICL scenarios, demonstrating that simple SSMs, equipped with input and output-dependent processing, can accurately mimic gradient descent when data is presented as a sequence. This theory offers an explanation for the capacity of modern SSMs to execute ICL and outlines a network circuit capable of handling such tasks. Empirical experiments confirm that the proposed circuit can effectively learn and perform small-scale synthetic tasks in practice." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(1) The authors have **overlooked important related work in this domain**. For example, [1] presented at NeurIPS 2016, demonstrates that **RNNs can perform gradient descent during the forward pass**. Additionally, Section 3.1 in the current paper shares several similarities with Section 2 of [1]. To be clear, this is not an accusation of plagiarism, but rather an indication of missing key references. While there is a difference in scope (RNNs versus SSMs), this oversight reduces the originality of contribution #1. I kindly ask the authors to specify the principal differences between the approach taken by [1] and the approach used by SSMs in their work to better highlight the novel contributions. \n\n(2) **Overlooks simple alternative approaches:** While the theoretical analysis is accurate, the authors overlook significant alternative approaches. References [2-4] demonstrate the connection between S6 layers and attention, showing that S6 can be more expressive than attention without the softmax. Additionally, various ICL studies for transformers omit the softmax (see [5-6] as an example), allowing direct conclusions that could extend to SSMs. **Given the extensive exploration of ICL capabilities in transformers, discussions on the ICL potential of SSMs should consider this reduction approach**. I recommend that the authors evaluate whether this approach could yield additional theoretical developments to strengthen their analysis. Moreover, I suggest that the authors explicitly state the advantages of their approach compared to the proposed alternatives. For instance, Section 3 introduces specific circuits that cannot be achieved through simple reductions. \n\n(3) **Understanding ICL in SSM variants can be enhanced** by examining their sub-components. Previous research indicates that S6 layers exhibit significantly better ICL capabilities than earlier SSMs [7]. However, while the authors highlight input and output-dependent processing as crucial features, they do not empirically ablate these features across various ICL tasks, nor do they provide a detailed theoretical analysis to substantiate this claim explicitly. I recommend adding a subsection that explores these aspects in depth. It is also important to note that input- and output-dependent processing can be implemented through various gating mechanisms. Hence, this claim could be considered somewhat ambiguous, as gated state-space models have been previously studied without demonstrating the same level of ICL capabilities as models like Mamba / S6.\n\n\n(4) **The claims regarding GD-SSM** (“Our construction, which we call GD-SSM, is not restricted to in-context learning tasks and performs well on general-purpose prediction problems”) do not hold, and much **more empirical analysis is required to justify** them.\n\n___\n\n[1] Learning to learn by gradient descent by gradient descent. Andrychowicz et al.\n\n[2] The Hidden Attention of Mamba Models. Ali et al.\n\n[3] Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality. Dao et al.\n\n[4] Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks. Sieber et al.\n\n[5] Transformers Learn In-Context by Gradient Descent. Oswald et al. (see section 2)\n\n[6] Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers. Dai et al. (see section 3.1)\n\n[7] Can mamba learn how to learn? a comparative study on in-context learning tasks. Park et al." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Can you explicitly define GD-SSM? Perhaps even provide pseudo-code? What are its learnable parameters? It is defined in 283 after the fact, but I think a more explicit definition would be helpful.\n\nQuestions related to experiments and first two bullets in Weaknesses section above:\n\n2. How do the results in this paper explain the success of recent SSM variants (as claimed in lines 488)? Line 052-053 say : \"Which features of these successful models contribute to in-context learning, as opposed to earlier variants? Using a constructive approach, we pinpoint input-dependent input and output processing, as the key features required for in-context learning\". I hoped the paper would provide insight into this question. However, it seems that the constructed GD-SSM is quite different from the currently used SSM architectures (e.g. Griffin, Mamba), and performs much better empirically in the experiments. Meanwhile the currently used SSM architectures do not seem to perform regression in-context learning (at least for one layer). So how do the results in this paper answer (or point to answering) the first question in line 052 or support the claim in line 488? This is my main question regarding this paper. I list some additional questions below that are an attempt to clarify some of the presented empirical results below.\n\n3. Why does Griffin do so poorly compared to linear transformers and S5? Shouldn't Griffin be a mix of sliding window attention and SSM (the RG-LRU), making it similar to the GD-SSM formulation? Or is the model referred to as Griffin just RG-LRU?\n\n4. Why does the time-invariant S5 appear to consistently outperform the input-dependent Mamba and Griffin models (even though all fail to converge)? I would have expected the models with input-dependent dynamics to perform ICL better. Is this difference at all interesting?\n\n5. Does it benefit the other architectures (1 layer linear attention, S5, Griffin, Mamba) to also provide them with the local self-attention preprocessing? Shouldn't they then be able to potentially learn the GD-SSM model if provided this? If not, why not?\n\n6. Can 2 layers of the SSMs solve the task? Note that combining the local self attention with the diagonal SSM is a combination of 2 sequence processors.\n\n\nOther questions and comments:\n\n7. Where is the 1-step GD in Figure A?\n\n8. In Figure 4B, the 1 or 2 layer GD-SSM formulation always seems to achieve lower loss than the corresponding number of steps GD model. Why is this? What happens if we compare more layers and more steps, e.g. 5 or 10 steps/layers? \n\n9. Note that the color scheme in Figure 4C etc is hard to read and tell which line corresponds to which model.\n\n10. Note that LSA is first introduced in equation 2, but only later defined in line 119." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- To my knowledge, the insight viewing SSMs as a gradient accumulator allowing them to emulate gradient descent on in-context learning tasks is novel and the combination with local self-attention for preprocessing is interesting\n- The mathematical theory appears to be sound\n- The presentation of the material and step by step walk through of the theory from simple cases to more complex is clear and helpful\n- The theoretical findings potentially point to a mechanistic understanding of architectural requirements (for SSMs or other models) that enable types of in-context learning" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper investigates how state-space models (SSMs) can perform in-context learning through gradient descent. The authors provide both theoretical and empirical evidence that SSMs augmented with local self-attention can emulate gradient descent on implicit regression models. Their key insight is that the diagonal linear recurrent layer in SSMs can act as a gradient accumulator." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- While the paper does a good job of explaining the theory and formulation of GD-SSM and empirically validating GD-SSM on regression tasks, I didn't feel like it shed that much insight into the currently used SSM variants used in practice.\n - Note that line 488 says: \"These findings not only explain the success of recent SSM variants in in-context learning tasks but\nalso provide valuable insights for the design of future sequence models.\" \n - Note that Lines 052-053 say, referring to the modern SSM variants : \"Which features of these successful models contribute to in-context learning, as opposed to earlier variants? Using a constructive approach, we pinpoint input-dependent input and output processing, as the key features required for in-context learning\". \n - But I do not think the current version of the paper supports either of the claim that this paper sheds light on these questions \n - The approach constructed seems to be very different from those used in practice. In addition, the methods commonly used in practice do not appear to do well on the regression ICL tasks in this paper. So it is unclear what I should take away from this related to prior SSM methods?\n - I think the empirical results were an opportunity to provide insight here, but didn't seem to fully achieve this. Please see my questions below which may clarify this for me.\n\n- Related to the above, the experimental section is light on architectural details, making it hard to determine what exactly is being compared empirically and what conclusions can be drawn. Please include more experimental details including the architectures and hyperparameters.\n\n- The paper is often unclear on the terminology of local self-attention and local linear self-attention. The formulation in Section 3.2 appears to only require local linear self-attention, yet other times in the paper local self-attention is used. In the related works section the two are contrasted. I would recommend being very explicit and consistent on this point as the two are very different. \n\n- The paper is limited to regression style in-context learning. This is interesting and amenable to theoretical analysis, but also limits the impact of the investigation. See https://arxiv.org/abs/2401.12973 and https://arxiv.org/abs/2402.04248 for other papers that have empirically investigated other types of in-context learning in different architectures." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "To the best of my understanding and as mentioned above, the results presented here are a subset of the results presented in Zucchet et al. 2023. Can the authors compare their work to that paper and highlight what their insights are?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The paper is overall well written and easy to follow. The theoretical part is sound and experiments convincingly demonstrate the paper's claims in toy settings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In-context learning (ICL) is one of the surprising capabilities of LLMs at scale. Seminal results have shown that Transformer-based architectures have this ability and more recent ones confirmed that SSMs also do. This work studies shows that SSMs can implement ICL by gradient descent. They provide a constructive proof showing that 1 layer can implement one GD step and confirm empirically on toy tasks that the networks find this solution." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I am concerned by the novelty of this paper. [Zucchet et al. 2023](https://arxiv.org/abs/2309.01775) show that 1 SSM layer can implement any linear self-attention layer. This results implies that any ICL algorithm LSA can implement, an SSM can. This holds for 1 GD step studied in this paper, but also for any other algorithm the community has been studying over the last few years. Additionally, this paper also have very similar experiments to the ones presented here." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "1. See Weakness.\n2. The behavior of GD and 1-D GD-SSM appears different. Could the authors provide an explanation for this discrepancy? Clarifying this would help the readers better understand the distinctions in learning dynamics between these approaches.\n3. Figure 3: The font size in Figure 3 is too small and could be increased for readability." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. The paper demonstrates that the state-space model with LSA can learn in-context learning tasks on linear and nonlinear regression problems. While I do not dispute the claim that state-space models can achieve in-context learning via gradient descent, my concern lies in whether the specific modification introduced warrants the detailed calculations provided. The conclusions, as presented, seem to offer limited insights into how this work might advance research on improving in-context learning capabilities. A clearer connection to meaningful improvements in this area would significantly enhance the paper's contribution.\n2. The experiments compare the state-space model with Griffin and Linear Transformer, but they are restricted to shallow architectures of 1 or 2 layers. This setup is inconsistent with typical in-context learning scenarios, where models need to be sufficiently large for emergent phenomena and meaningful in-context learning capabilities to surface. The current experiments do not effectively capture these dynamics, making it difficult to observe such phenomena in shallow sequence models. Expanding the experiments to include deeper architectures would provide a more realistic assessment of in-context learning in state-space models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper imitates Transformers learn in-context by gradient descent(https://arxiv.org/abs/2212.07677). This paper proves that a single structured state-space model layer with local self-attention can reproduce the outputs of an implicit linear model with least square loss. (The task considered is not general)" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The paper addresses a theoretical statement about the transformer model, specifically that it can learn in-context through gradient descent. However, this concept is already well-known and documented. In theoretical research, it is widely recognized that transformers, convolutional models, and recurrent models, such as state-space models, are universal approximators and are capable of learning continuous target relationships. Therefore, demonstrating the same for state-space models does not appear to offer significant theoretical advancement. If the authors wish to underscore the importance of this work, I would recommend showing that previous work in approximation theory does not extend to the in-context learning case. Without this distinction, the contribution seems to fall within a subset of known results that hold limited value for theoretical study. \n2. The notion of in-context learning, as presented, lacks practical interest. Simply stating that a model \"can learn\" through in-context learning is insufficient, as the same argument could be made for various methods, including Newton's or quasi-Newton's methods. There is no compelling reason for practitioners to assume that, when state-space models engage in in-context learning, the behavior in terms of loss convergence or asymptotic rates would align with that of gradient descent. Clarifying this distinction would strengthen the paper’s contribution. Could you provide empirical comparisons of convergence rates or asymptotic behavior between your method and alternatives like Newton's or quasi-Newton's methods.\n3. The paper introduces a state-space model with local self-attention, which is not a commonly adopted approach in practice. It would be more beneficial to align the framework with models that are widely used in real-world applications. A method akin to the linear transformer might be more appropriate and could provide a better point of reference for practical utility." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "the mechanism of in-context learning in state-space models corresponds to gradient descent" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024statespace,\ntitle={State-space models can learn in-context by gradient descent},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=52XG8eexal},\nnote={under review}\n}" }, "abstract": { "value": "Deep state-space models (Deep SSMs) have shown capabilities for in-context learning on autoregressive tasks, similar to transformers. \nHowever, the architectural requirements and mechanisms enabling this in recurrent networks remain unclear. \nThis study demonstrates that state-space model architectures can perform gradient-based learning and use it for in-context learning.\nWe prove that a single structured state-space model layer, augmented with local self-attention, can reproduce the outputs of an implicit linear model with least squares loss after one step of gradient descent.\nOur key insight is that the diagonal linear recurrent layer can act as a gradient accumulator, which can be `applied' to the parameters of the implicit regression model.\nWe validate our construction by training randomly initialized augmented SSMs on simple linear regression tasks. The empirically optimized parameters match the theoretical ones, obtained analytically from the implicit model construction. \nExtensions to multi-step linear and non-linear regression yield consistent results.\nThe constructed SSM encompasses features of modern deep state-space models, with the potential for scalable training and effectiveness even in general tasks. \nThe theoretical construction elucidates the role of local self-attention and multiplicative interactions in recurrent architectures as the key ingredients for enabling the expressive power typical of foundation models." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "state-space models", "in-context learning", "linear recurrent networks", "mesa-learning" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/66239f66cc8f26c06fb2944bd73389844224621a.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "State-space models can learn in-context by gradient descent" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "None" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please address the concerns I raised in the weaknesses section. Additionally, I recommend revising the introduction to better reflect the paper's contributions. In my view, the primary contribution is the proposal of the geometric counterpart of NGNN and the proof that this approach effectively resolves the limitation of DisGNN in identifying **symmetric point clouds when the graphs are even fully connected**. \nThe authors should also consider relevant experiments in this direction to emphasize the novelty and significance of this work." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The paper attempts to address a crucial problem that enhances our understanding of the potential of invariant neural networks and can guide future model design.\n- Investigating the geometric counterparts of subgraph GNNs is a novel contribution.\n- The results extend beyond specific cases, such as asymmetric point clouds, broadening our understanding of how these models perform on symmetric point clouds as well." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper explores the geometric completeness of a significant class of geometric deep learning models: invariant neural networks. These networks leverage invariant features to impose strong inductive biases on spatial information, yet their theoretical expressive power remains somewhat unclear. This study aims to bridge that gap, enhancing both our theoretical understanding and practical application of these models.\n\nThe authors first demonstrate that incorporating distance into message-passing neural networks (like DisGNN) allows for the identification of asymmetric point clouds but struggles with highly symmetric ones. They then investigate geometric extensions of subgraph-based GNNs and prove that these models, specifically GeoNGNN, can successfully distinguish symmetric point clouds, achieving E(3)-completeness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The paper lacks clarity and structure in some areas. The detailed explanation of NGNN, which serves as the backbone of their main contribution, the GeoNGNN framework, is left in the appendix. I recommend the authors integrate key aspects of NGNN, such as its core equations or an architectural diagram, into the main text. Additionally, including a comparison with the original DisGNN would be helpful—highlighting the differences and explaining what enables NGNN (intuitively) to overcome the limitations of DisGNN. This would give readers a clearer understanding of how the proposed approach builds on previous work and addresses specific challenges without disrupting the flow. Instead, a brief overview of NGNN, along with key formulas and a comparison with GNN, could benefit the flow of the whole paper.\n\n- The results are primarily constrained to cases with global connectivity, which is often impractical in real-world applications due to the significant computational costs. Several studies [1], [2], [3], [4] have explored scenarios where the graph is not fully connected, underscoring the need to evaluate the performance of invariant neural networks in sparse graph settings. In practice, invariant neural networks tend to perform worse than equivariant ones in these cases. While the authors have left these cases in the future direction, it would greatly strengthen the paper if they could extend their analysis to sparse graphs or at least discuss how their completeness results may vary with different levels of graph sparsity. Providing theoretical bounds on performance degradation as connectivity decreases would also be valuable.\n\n- The experimental results, while showing some improvement, are relatively marginal, which limits the empirical impact of the work. I suspect this might be due to the sparsity of the graphs used in practical applications. The authors should aim to demonstrate the significance of their approach by clarifying in which specific cases their method outperforms existing methods. Providing examples or scenarios where GeoNGNN has a clear advantage would strengthen the empirical contributions.\n\n\n[1] Wang, L., Liu, Y., Lin, Y., Liu, H., & Ji, S. (2022). ComENet: Towards complete and efficient message passing for 3D molecular graphs. Advances in Neural Information Processing Systems, 35, 650-664.\n[2] Joshi, C. K., Bodnar, C., Mathis, S. V., Cohen, T., & Lio, P. (2023, July). On the expressive power of geometric graph neural networks. In International Conference on Machine Learning (pp. 15330-15355). PMLR.\n[3] Wang, S. H., Hsu, Y. C., Baker, J., Bertozzi, A. L., Xin, J., & Wang, B. (2024). Rethinking the benefits of steerable features in 3D equivariant graph neural networks. In The Twelfth International Conference on Learning Representations, ICLR\n[4] Sverdlov, Y., & Dym, N. (2024). On the Expressive Power of Sparse Geometric MPNNs." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "1) The excessive use of bold text and the absence of a clear outline make the paper’s contributions difficult to discern. Could the authors consider restructuring the introduction for better clarity?\n\n2) I find that the statements in the section **Theoretical Characterization vs Practical Use** rely on the example C.2. How increasing the sparsity beyond this simple example is understudied despite the authors strong claims that relaxing the fully-connected condition leads to better expressiveness of GeoNGNN compared to DisGNN.\n\n3) There is no supporting evidence for GeoNGNN over existing architectures in the primary paper. Additionally, there is no comparative analysis involving node feature information generated by a complete invariant function. Could the authors address this gap?\n\n4) A significant portion of the QM9 dataset consists of non-symmetric structures. What are the proportions of indistinguishable data restricted to the subset of QM9 that includes only symmetric structures?\n\n5) In the QM9 noise study, the significant reduction in non-distinguishable point clouds occurs near what appears to be the level of reported error in the QM9 dataset. Given the reported error of 0.1Å, how is this error rescaled based on the applied scaling coefficient? \n\n6) Distinguishing structures on QM9, which lacks conformers, does not seem to be as important as datasets which contain conformers or very nearly isomorphic point clouds.The most compelling analysis appears to come from the study of MD17 but with mixed results. GeoNGNN appears to do particularly well on Benzene which is highly symmetric. How does Benzene behave under the noise tolerance study?\n\n7) Typically, ModelNet40 is sampled to avoid handling large point clouds. It is unclear from the text whether the entire mesh or a sampled version is used. If sampled uniformly, there is no guarantee that the symmetries are preserved. Could the authors clarify this in the text?\n\n8) The selection of ModelNet40 does not seem to rigorously test the theoretical claims of the paper, which focus on nearly isomorphic point clouds. Could the authors provide more rigorous testing on datasets that better align with their theoretical focus?\n\n9) It is unclear from the text and appendix what each structure in the synthetic dataset represents, how these structures were constructed, and why they are significant. Could the authors provide more detailed explanations on the construction and relevance of these synthetic structures?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The paper introduces a novel conceptual framework for understanding the efficacy of certain invariant architectures. This is further supported through theoretical analysis and empirical studies. Additionally, it proposes a framework for the development of future architectures extending the impact and significance of the work." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The paper offers the following contributions:\n1) Introduces and defines the notion of \"Identify\" for invariant GNNs, positioned between distinguishability and completeness.\n2) Provides a characterization for the incompleteness of DisGNN\n3) Proposes GeoNGNN to ensure indentification of the cases where DisGNN is incomplete\n4) Demonstrates that several established invariant GNNs are capable of completeness" }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The paper lacks sufficient empirical evidence to support its theoretical analysis, significantly reducing the overall significance and impact of the work. The selected real world experiments emphasize datasets which lack conformers or nearly isomorphic point clouds. Furthermore, the main text does not provide adequate evidence to demonstrate the advantages of GeoNGNN over the existing complete invariant architectures.\n\nAdditionally, the excessive use of bold text and the absence of a clear outline in the introduction make it challenging to follow and clearly understand the contributions of the paper." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- How does this method for expressivity generalize to different types of architectures? To the reviewer it seems that this method for showing is very specific and would be difficult to generalize to other types of equivariant architectures acting on point clouds." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "The authors have provided both extensive proofs as well as extensive analysis\nto their claims. Overall the presentation and intend is clear and definitions\nare well-thought out and the authors provide a good heuristic insight with each\nintroduced theorem and definition which is nice. The extensive analysis of both\nDisGNN and GeoNGNN shows that the work is of good quality and looks to be of\ngood quality to the reviewer. All theorems come with extensive proofs and with\na intuition which is helpful for the non-mathmatical audience. The quality of\nthe content, such as originality and potential impact, is harder to asses since\nthe reviewer is not familiar expressivity research." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors of the paper prove that certain families of models are not only\ninvariant with respect to the Euclidian group and permutation group, but also\nthat classes of models distinguish the orbits of $\\mathbb{R}^3$ under the\naction of $E(3)$. An extended analysis of the expressivity of DisGNN is\nprovided and it is shown that this network architecture is nearly $E(3)$\ncomplete. As a last contribution an analysis is provided for various families\nof neural networks and conditions are provided under which they are\n$E(3)$-complete. The theoretical results are verified by experiments\non the QM9 dataset and a synthetic dataset with designed edge cases." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "To the reader it seems that some of the definitions are somewhat convolved and\nsome simplification and clarity in the definitions might improve reading. Some\nof the definitions, while they might be customary in the machine learning\nliterature, are somewhat unfortunately choses from a mathematical perspective.\nCompleteness of a space in the mathematical sense implies that each Cauchy\nsequence has a limit within that space. A second example is the use of the term\nisomorphism. While not wrong, a better phrasing is to say that the two point\ncloud lie in the same orbit with respect to the action of the Euclidian group\nacting on the tensor product of copies of $\\mathbb{R}^3$. The current phrasing\nmight be better if is more in line with terminology used in machine learning." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": { "value": "NA" }, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "I have two questions.\n\n1. Do you have any insight on achieving E(3)-completeness for frame-based approaches?\n\n2. Can you comment on achieving E(3)-completeness by using node features beyond pairwise distances, e.g., dihedral angles?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The paper symmetrically studies the problem of E(3)-completeness geometric graph neural networks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the expressiveness power of message-passing neural networks incorporating pairwise distance between graph nodes, showing the near E(3)-completeness. Furthermore, the authors study the subgraph graph neural networks, which can achieve E(3)-completeness. Therefore, it is possible to make DimeNet, GemNet, and SphereNet to achieve E(3)-completeness." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The work is based on global connectivity assumption, and this assumption significantly limits this work. Also, the experimental results seem to be quite weak." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024on,\ntitle={On the Completeness of Invariant Geometric Deep Learning Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=52x04chyQs},\nnote={under review}\n}" }, "abstract": { "value": "Invariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features in point clouds. These models are characterized by their simplicity, good experimental results and computational efficiency. However, their theoretical expressive power still remains unclear, restricting a deeper understanding of the potential of such models. In this work, we concentrate on characterizing the theoretical expressiveness of a wide range of invariant models. We first rigorously characterize the expressiveness of the most classic invariant model, message-passing neural networks incorporating distance (DisGNN), restricting its unidentifiable cases to be only highly symmetric point clouds. We then prove that GeoNGNN, the geometric counterpart of one of the simplest subgraph graph neural networks, can effectively break these corner cases' symmetry and thus achieve E(3)-completeness. By leveraging GeoNGNN as a theoretical tool, we further prove that: 1) most subgraph GNNs developed in traditional graph learning can be seamlessly extended to geometric scenarios with E(3)-completeness; 2) DimeNet, GemNet and SphereNet, three well-established invariant models, are also all capable of achieving E(3)-completeness. Our theoretical results fill the gap in the expressive power of invariant models, contributing to a rigorous and comprehensive understanding of their capabilities." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "geometric deep learning", "invariant models", "completeness", "expressiveness", "graph neural network", "subgraph graph neural network" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/111ba0fcd0bad3a6f6d645480e99ea80ad5eb8a4.pdf" }, "presentation": null, "primary_area": { "value": "learning on graphs and other geometries & topologies" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/3f888d50a7efed07e6bcf0f186b6ddf1c0f2f117.zip" }, "title": { "value": "On the Completeness of Invariant Geometric Deep Learning Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "See weaknesses." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. This paper have a strong theoretical foundation with mathematical proofs about the error bounds of using dense adapter for sparse LLMs. It emphasize on the gradient interference, and mitigate this error for a better PEFT method for sparse models.\n\n2. Novel identification and analysis of the gradient interference problem. This also results in a novel PEFT method.\n\n3. Experimental results show its advantage in comparison to LoRA.\n\n4. This paper is well-written." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduce a comprehensive theoretical framework for memory-efficient fine-tuning for sparse LLMs. The authors identify and analyze a key challenge called \"Sparse Weight Gradient Interference,\" where masked pre-trained weights and PEFT weights exhibit competing optimization objectives during fine-tuning. To address this, they propose a novel method combining three key innovations: a pooling-based PEFT method, normalization of PEFT modules, and an adaptive layer-wise approach using Centered Kernel Alignment for sparsity allocation. Their theoretical analysis identifies three crucial factors affecting fine-tuning efficacy: errors from weight norms, PEFT structures, and error accumulation during fine-tuning. The effectiveness of their approach is demonstrated through extensive experiments on LLaMA-2 models, showing superior performance compared to existing methods, particularly in maintaining model performance under high sparsity conditions (up to 70%), while providing theoretical guarantees for error bounds." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. Lack empirical upper bound: The paper lacks a crucial empirical upper bound comparison. Since the core problem stems from using dense adapter weights with a sparse model, an ideal oracle baseline would be directly fine-tuning only the sparse positions in the model. Including this oracle baseline would provide a clearer understanding of the maximum achievable performance without gradient interference. This comparison would also serve as a valuable reference point for future research in sparse fine-tuning methods.\n\n2. Limited model diversity in experiments: This work only evaluate LLaMA-2 family model. I will recommend the authors to further evaluate LLaMA-3 family and Mistral family for a more comprehensive comparison.\n\n3. Lack practical efficiency: While the proposed method show better performance, the authors do not provide any efficiency results to show the practical training speed of the proposed method. The proposed method cannot be useful if it is slow even with strong theoretical foundation. Therefore, I think the authors should provide the number of trainable parameters, the training time, the training memory for LoRA and the proposed method.\n\n4. Typo in Table 2: I don't see any other \"LoSA\" in this paper. The authors should either delete it or add an definition for it.\n\n5. Lack of ablation studies: This paper do not do ablation study for the Iterative Sparse Fine-Tuning Scheme. This should be added to verify its effectiveness.\n\nI will consider to raise my score if more evidence are provided about the effectiveness of the proposed method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Wanda does not require updating the model weights during pruning unlike the proposed method and SparseGPT. So if Wanda is used as a baseline here, is it also fine-tuned using the same amount of data as this method for fair comparison? \n2. It is not clear in line 4 of algorithm 1 why the sparsity mask is updated using either SparseGPT or WANDA\n3. Given that this method uses PEFT and does not have to update all the model parameters like SparseGPT, can you report the amount of time and memory required to run your method and compare it against other baselines too? This might bolster your case further.\n4. Could you provide some insights on why is your method so much better than SparseGPT given that the latter updates the weights of the entire model?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Allocating additional fine-tuning parameters to the layers with higher reconstruction loss is novel.\n2. They demonstrate that the zero-shot performance of the model after pruning is the best when compared to the other baselines on various tasks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "A common method to prune iteratively is to learn the mask and also update the model weights to recover the loss induced by pruning using fine-tuning. The mask is applied such that the reconstruction loss of each layer after applying the mask is minimized. Rather than regular fine-tuning, this method employs LoRA to heal the model. They posit that using LoRA to heal the model introduces errors owing to the \"Sparse Weight Gradient Inference\" problem. This occurs because the LoRA module is not aware of where the masks have been applied to the frozen pre-trained model. Thus, it could have gradients for parameters where the frozen pre-trained model is pruned and set to 0 resulting in interference. They estimate the error that using LoRA incurs. \n\nThe proposed method uses bilevel optimization where in the upper level the mask is learnt and in the lower level the weights are updated using a modified LoRA. The modified LoRA applies a pooling operation on the input to reduce it to a lower dimension g, followed by multiplying the resulting value with weight G and then projects the output back to the original dimension. The weights G are learnt in this PEFT variant. To further alleviate the error introduced by using LoRA to heal is to rein in the magnitude of weight change by using normalization such as weight-decay or drop out etc. Rather than introducing the parameters uniformly to all layers for fine-tuning, more parameters are allocated to layers with higher reconstruction loss. The layerwise sparsity rate is set based on the Hilbert-Schmidt Independence Criterion metric and is inversely proportional to it, thereby pruning layers with higher information redundancy. This whole process is repeated for several iterations and the layer-wise sparsity rate, the sparsity mask and model weights are updated at each iteration." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. While the sparsity is induced by applying masks, unstructured pruning does not reduce the latency. In real world applications, reducing the latency is also crucial. Could you report the latency improvements of the final model and how it compares to just using N:M sparsity and WANDA etc?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "- How does the proposed method perform for structured or semi-structured sparsity?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The authors point out the Sparse Weight Gradient Interference (SWGI) phenomenon, attributing to the mismatch between the sparse structure of the model weights and the dense structure of the PEFT weights.\n\n- The authors provide a theoretical analysis of the SWGI phenomenon and related bounds on the errors introduced by sparsification." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "In this paper, the authors focus on the sparsification and PEFT recovery of LLMs. The authors identify and address the Sparse Weight Gradient Interference (SWGI) phenomenon, where gradients from masked weights interfere with the fine-tuning of active parameters, leading to performance degradation. The authors conduct a theoretical analysis on this problem, and propose a new new iterative sparse fine-tuning scheme to handle this problem. Experiments on benchmarks show that the proposed method recovers the accuracy better than LoRA." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The structure and readability of the paper could be improved. Also, a main figure illustrating the problem setting or the SWGI phenomenon, and the proposed method could be helpful.\n\n- A detailed ablation study on the many components that comprise the method (CKA-guided sparsity setting, PEFT parameter allocation, sparsity scheduling) is missing.\n\n- More experiments on well-accepted benchmarks such as MMLU or GSM8K are needed to verify the effectiveness of the proposed method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Please see the weaknesses section." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The theoretical examination of Sparse Weight Gradient Interference in sparse PEFT methods is sound and provides valuable insights.\n- The proposed method outperforms LoRA in specific method-benchmark combinations.\n- The improvements apply across different levels of sparsity and sizes of language models." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper provides a theoretical examination of parameter-efficient fine-tuning (PEFT) for sparse large language models (LLMs). The authors explore Sparse Weight Gradient Interference, identifying large weight norm as a potential main source of loss error in general sparse PEFT methods. Additionally, they suggest that the LoRA structure contributes to loss error in sparse PEFT and propose an alternative PEFT approach consisting of three steps—pooling, linear transformation, and expansion—which they argue achieves a tighter upper bound on loss error compared to LoRA. They also raise the possibility of error accumulation over fine-tuning iterations as a further source of loss. Alongside their theoretical insights, the authors present a brief empirical analysis showing that their sparse PEFT method can outperform LoRA across certain benchmarks when combined with the sparseGPT and Wanda LLM pruning techniques." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The paper lacks detailed experimental settings, making it unclear if the comparison with the baseline is entirely fair. Key details such as the number of data points used for LoRA, the number of fine-tuning iterations, hyper-parameters, and any tuning performed (particularly for learning rate) are missing.\n- The empirical evaluation is limited to classification tasks, and additional open-ended generation downstream tasks would strengthen the assessment of the proposed method.\n- Even on the limited set of reported downstream tasks, improvements are inconsistent, with LoRA outperforming the proposed method on certain model-task combinations, such as Llama2(13b)-wanda.\n- There is no discussion on result variance; only single values are reported. Given the minor improvements observed, additional experiments with varying random seeds are needed to allow readers to assess the method's efficacy more reliably.\n- The statement 'Empirical evaluation on a 50% sparse LLaMA-2 7B model demonstrates the superiority of our approach, achieving lossless compression' in abstract is misleading and not supported by the results." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024mitigating,\ntitle={Mitigating Gradient Interference for Efficient Sparse Fine-Tuning of Large Language Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=53MDeiZ9mC},\nnote={under review}\n}" }, "abstract": { "value": "Large Language Model (LLM) sparsification plays a crucial role in model compression. \nAmong various methods, training-free approaches are highly efficient but often result in accuracy loss, while full fine-tuning requires substantial computational resources. \nRecent works have begun exploring sparse Parameter-Efficient Fine-Tuning (PEFT) methods, but lack theoretical guidance.\nThis study presents the first comprehensive theoretical framework for efficient sparse fine-tuning, addressing a critical gap in the literature. \nSpecifically, we identify gradient conflict as the primary issue in PEFT sparse methods, wherein masked pretrained weights and corresponding PEFT weights exhibit competing optimization objectives during fine-tuning, potentially compromising model performance.\nWe theoretically model this phenomenon and identify three key factors influencing the efficacy of fine-tuning in sparsified LLMs: (1) error introduced by weight norms, (2) error composition from PEFT structures, and (3) error accumulation during fine-tuning.\nLeveraging these theoretical insights, we propose a novel iterative sparse fine-tuning scheme that systematically addresses each identified factor. \nWe implement an iterative process alternating between sparsity and fine-tuning to mitigate accumulated error in single turn of finetuning. \nWe employ pooling instead of low-rank decomposition to reduce error composition from PEFT structures. \nWe apply normalization to PEFT modules during fine-tuning, constraining error values by limiting weight norms while preserving representational capacity. \nAdditionally, we utilize Centered Kernel Alignment based information similarity assessment for adaptive allocation of layer-level sparsity and PEFT parameter quantities, addressing layer-specific redundancy.\nEmpirical evaluation on a 50\\% sparse LLaMA-2 7B model demonstrates the superiority of our approach, achieving lossless compression." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Large language models", "Sparse" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/aa6cb68df15e956512a954ca7504429b64680816.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": null, "title": { "value": "Mitigating Gradient Interference for Efficient Sparse Fine-Tuning of Large Language Models" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Most questions are in the weaknesses. But some comments about writing:\n- Citations should be \\citep{}, it seems like they are not, and are instead inline\n- [Minor] It would be nice to see some examples in the appendix, in terms of passage it came from and question generated." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The new proposed task is an interesting contribution, and to my knowledge has not been studied before\n- They use a wide variety of retrieval model architectures, from cross-encoders, to generative retrieval, spare retrieval, and dense retrieval.\n- Their proposed model does better on the data they generated" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a new task for retrieval models: that of finding the relevant section title in a book, given a question. They build a dataset for this task by using PDF parsing to extract the table of contents from books. They generate question for the task through the use of Mixtral. \n\nThey then train a model to do well on this task, called Louis. Their model is trained on the training set (also generated with Mixtral) and shows improved performance over other model types, including non-fine-tuned versions of DSI, BM25, and Mistral. They show an error analysis where their model performs better." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. There is a lack of important details. How many chunks/passages are there in the corpus for dense retrieval (or BM25)? Does each chunk for these models also get the section, so that the model can use that information? Why are there no long context models evaluations (Gemini/Prolong/even just a 128k Llama)? Does Mistral see the entire book or just the ToC? etc.\n2. Conceptually, performing ToC retrieval is strictly easier than searching for the specific passage that a question was generated from. From related work on searching for the relevant passage in long documents (see the \"Scrolls Benchmark\" [1] for task examples) IR models perform pretty well with retrieval (see the LoCo benchmark [2]). It seems something is wrong with the setup if the models are doing this poorly. My guess is that the book is chunked but not given the section headers in the chunks, so they lack that context. Furthermore, BGE is a decent baseline, but pretty weak in comparison - see the MTEB leaderboard where BGE-base (I assume because of the 768 dim vector, but again not described) is ranked #47.\n3. Overall, the task does not appear very challenging. R@3 is 90% and close to that for many baseline models (DSI, BM25). If BM25 can get nearly the same performance as the proposal model, it does not seem like much of an improvement (and is perhaps not even statistically significant).\n4. There is no analysis on the false negative information in these questions. It is likely that many questions are answered in several places and that falsely lowers the score. I would guess that if the authors did an analysis of the Recall @ 3 failures that the answer is given in both places. This makes (3) more of an issue and lowers the quality of the dataset.\n5. The main modeling contribution seems to be that using the same data generation process as making the test set, and then by training on it, makes a model better able to perform this task. This is unsurprising and demonstrated over and over in the literature on every modality. This would be totally fine, if this was the only issue, but I assume that this means that the proposed Louis model cannot do another other tasks, as it is likely overfit for this. \n\nOverall, I think the task could be useful for the retrieval community as a useful long-context evaluation. However, I am unconvinced of the datasets quality and there appear to be modeling concerns (or at least a lack of information).\n\n[1] Scrolls Benchmark: https://www.scrolls-benchmark.com/tasks\n\n[2] LoCo benchmark: https://arxiv.org/pdf/2402.07440v2" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 1 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 1 }, "primary_area": null, "questions": { "value": "The questions are listed in the 'Weaknesses' section." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. The paper introduces a new dataset containing queries and their corresponding subsections, which may contribute to research in document retrieval.\n2. Constructing the dataset and conducting the experiments required substantial effort." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "Inspired by the way humans often search through the Table of Contents when reading books, this paper introduces a new task: given a query and the Table of Contents (TOC) of a long document, the objective is to retrieve the correct subsection in the TOC that contains evidence to answer the query. The authors introduce and release a new multi-domain dataset, ToCTome, which consists of 18 books across 6 domains. For each subsection, they use the Mixtral 8x7b model to generate questions, forming subsection-query pairs. Additionally, they split the data into training, development, and test sets, and fine-tune Mistral Instruct v0.2 with the LoRA adapter. Experimental results demonstrate that the fine-tuned Mistral Instruct v0.2 achieves an impressive R@1 score of 82.6%, outperforming BM25, dense retrieval models, and the original, non-fine-tuned Mistral Instruct v0.2 on this task. These findings highlight the strong capabilities of LLM in ToC-based retrieval." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The authors introduce a new problem but do not explain its importance and significance. The purpose of using a large language model (LLM) to locate the subsection relevant to the query is unclear. The authors simply state that this \"mimics how humans leverage the structure of a book,\" but they don’t explain why this is necessary. I believe the authors should address this question: \"After finding the subsection relevant to the query, what can be achieved?\"\n2. The authors assume that each query can be matched to a corresponding subsection in the Table of Contents. However, in real-world scenarios, many queries may not align with the Table of Contents; in particular, some fine-grained questions cannot be mapped to a specific subsection.\n3. The proposed dataset and method are designed for queries that correspond to a single subsection. However, some queries may span multiple subsections, which this dataset and method do not account for. This results in oversimplified problem modeling.\n4. The writing of this paper has several issues: (1) Many sentences do not follow an academic tone, and exclamation marks are used frequently, which is uncommon in academic papers. (2) Detailed experimental results are repeated in the ABSTRACT, INTRODUCTION, and EXPERIMENTS sections." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. Can the methods developed for Loius be adapted for general retrieval tasks beyond structured documents like books? If so, what modifications would be necessary?\n2. Why were different maximum input lengths used for Loius and DSI in your experiments? Could this difference affect the comparative performance results reported?\n3. Given the reliance on a structured ToC, how does Loius handle content variations within sections that might not be accurately reflected by their ToC descriptions?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The innovative use of the ToC to guide the retrieval process introduces a unique approach to document navigation, potentially transforming retrieval strategies for structured documents.\n- Extensive experiments validate the system’s efficacy, particularly its ability to outperform traditional retrieval methods.\n- The creation of the ToCTome benchmark contributes a valuable resource to the research community, fostering further innovation in retrieval systems that understand document structure." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The research paper introduces Loius, a novel retrieval system that leverages the structure of ToC to enhance retrieval processes. By mimicking how humans intuitively use a ToC to navigate books, Loius proposes a fresh retrieval paradigm that contrasts with traditional methods based on keyword or vector-based searches. The paper also presents a new benchmark dataset ToCTome, which comprises 18 books across six diverse domains, providing a robust platform for evaluating ToC-based retrieval systems. The results demonstrate Loius’s superior performance in accurately locating relevant sections, boasting a Recall@1 score significantly higher than the next best system (DSI) and other baseline models like BM25 and DPR." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- The specific application of ToC-based retrieval to books may not generalize to other types of content that lack a clear hierarchical structure, such as unstructured web pages or documents without a ToC.\n- The technical novelty appears limited primarily to the adaptation of existing retrieval technologies to a new input structure (ToC), potentially limiting the method’s broader applicative insights.\n- In Section 5.2, I find that the maximum input length of Loius and DSI are significantly different. It is unclear if this difference may affect the experimental results." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "Can we instruction finetune an LLM to pick the best subsection to answer a user query by showing it a Table of Contents of a long document?" }, "_bibtex": { "value": "@inproceedings{\nanonymous2024loius,\ntitle={Loius (Look it up in the Structure): Benchmark and Techniques for Document structure aware {LLM} based Retrieval},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=53kUa92R7J},\nnote={under review}\n}" }, "abstract": { "value": "Modern day LLMs have shown remarkable success in understanding human instructions and provide satisfactory responses across a diverse set of tasks that require world knowledge. In this work, we leverage the inherent capability of LLMs to learn new tasks through instruction to design a novel retriever Loius that proposes a new retrieval paradigm: Table of Contents (ToC) based retrieval. Loius mimics how humans leverage the structure of a book (or a long document) by using ToC to search for information. Loius demonstrates that LLMs can be finetuned to build ToC based retrievers where the most granular ToC entry (leaf node) serves as the retrieval unit. This approach differs from traditional retrieval systems, which index chunks of information from long documents. We also introduce a novel\ncomprehensive benchmark ToCTome featuring 18 books across 6 diverse domains (with train, test and dev splits) and show that Loius achieves R@1 score of 82.6% as compared to the next best system (DSI) (76.9%) and other baselines such as BM25 (71.6%), DPR (53.6%) and out-of-the-box Mistral Instruct v0.2 (18.0%) on average. Loius is capable of incorporating corpus knowledge while learning to select the most relevant ToC leaf node, resulting in almost zero hallucinations (lessthan 0.05%) – a key issue with LLM-based retrievers. In addition to the benchmark, complete code for building Loius will be publicly released to accelerate research\nin the novel direction of ToC based retrieval." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "information retrieval", "llm", "model based retrieval", "document search", "retrieval benchmark", "document structure", "benchmark" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/93b973fae8da9e008b97f70fa474e199c1844263.pdf" }, "presentation": null, "primary_area": { "value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/77450c042f442e7fc21d225dfdc8f444e0bda0ab.zip" }, "title": { "value": "Loius (Look it up in the Structure): Benchmark and Techniques for Document structure aware LLM based Retrieval" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 5 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "Although there is no specific question that will surely affect my evaluation, some comments if any on the points listed in the Weaknesses section would be highly helpful." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "- The method is technically reasonable.\n- The idea is somewhat new. To my knowledge, using the resDMD objective with neural observables is quite natural but has not been exactly practiced yet.\n- The experiments nicely demonstrate the utility of the method." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "A method for computing the Koopman spectra of dynamical systems is proposed. It stands on two main ingredients: 1) the use of spectral residual as a loss function, and 2) the use of NNs for constructing observables. Its applications to a pendulum, turbulence, and neural dynamics are presented, and the proposed method is shown to be successful in extracting the Koopman spectra and analyzing the data." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "(1) Although the work is solid, I do not think the technical contribution is so significant to be included in the ICLR proceedings. The use of neural observables has been known and practiced well in these 8 years or so, and ResDMD has been already well known and discussed recently. The technical contribution of this work inevitably looks incremental.\n\n(2) The rich literature on the use of NNs as DMD observables, other than Li et al. (2017), seems to be overlooked. For example, even restricting the scope to the mere use of NNs for DMD-based analysis (i.e., excluding more applied perspectives such as control), the following papers (and probably more) should be relevant:\n\n- N. Takeishi, Y. Kawahara, T. Yairi: Learning Koopman invariant subspaces for dynamic mode decomposition, Advances in Neural Information Processing Systems 30, 2017, pp. 1130–1140\n- B. Lusch, J. N. Kutz, S. L. Brunton: Deep learning for universal linear embeddings of nonlinear dynamics, Nature Communications, vol. 9, no. 1, p. 4950, 2018\n- A. Mardt, L. Pasquali, H. Wu, F. Noé: VAMPnetsfor deep learning of molecular kinetics, Nature Communications, vol. 9, no. 1, p. 5, 2018.\n- E. Yeung, S. Kundu, N. Hodas: Learning deep neural network representations for Koopman operators of nonlinear dynamical systems, Proceedings of the 2019 American Control Conference, 2019, pp. 4832–4839\n- S. E. Otto, C. W. Rowley: Linearly recurrent autoencoder networks for learning dynamics, SIAM Journal on Applied Dynamical Systems, vol. 18, no. 1, pp. 558–593, 2019\n- O. Azencot, N. B. Erichson, V. Lin, M. W. Mahoney: Forecasting sequential data using consistent Koopman autoencoders, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 475–485\n- H. Wu, F. Noé: Variational approach for learning Markov processes from time series data, Journal of Nonlinear Science, vol. 30, no. 1, pp.23–66, 2020\n- D. J. Alford-Lago, C. W. Curtis, A. T. Ihler, O. Issan: Deep learning enhanced dynamic mode decomposition, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 32, no. 3, p. 033116, 2022\n- T. Iwata, Y. Kawahara: Neural dynamic mode decomposition for end-to-end modeling of nonlinear dynamics, Journal of Computational Dynamics, vol. 10, no. 2, pp. 268–280, 2023\n\nI do not think all of them should be included in the reference with detail, but at least the existence of such a rich literature should be mentioned to help readers to better understand the context of the research.\n\nBelow are relatively minor technical points that I found unclear.\n\n(3) The authors emphasize the \"lack of theoretical guarantee of convergence\" of EDMD for several times. In what sense is this \"lack\" supposed? For example, the work by Korda & Mezić:\n\n- M. Korda & I. Mezić: On convergence of extended dynamic mode decomposition to the Koopman operator, Journal of Nonlinear Science, vol. 28, pp. 687–710, 2018\n\ndiscusses the convergence in some sense.\n\n(4) The proposed method alternates between the gradient-based update of $\\theta$ and the least squares solution to get $K$. Is there any insight of this choice? I am asking this because it is also possible to include the least squares within the gradient computation, as done in Takeishi et al. (2017); Otto & Rowley (2019) listed above for example.\n\n(5) In the experiment, does EDMD-DL follow the exactly same configuration as NN-ResDMD except for the loss function? (it should.) Please elaborate on this more clearly to make it easier to assess the benefit particular to the proposed method." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "(1) How does this method differ from existing deep learning approaches for Koopman operator estimation, and what substantial improvements does it offer in terms of robustness, accuracy, or efficiency?\n\n(2) What are the differences and connections between the proposed loss function in this paper and existing evaluation functions like the VAMP score?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "The proposed NN-ResDMD method offers a deep neural network based approach for estimating Koopman spectral components" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose NN-ResDMD, a deep learning-based approach that directly estimates Koopman spectral components by minimizing a spectral residual. This method aims to improve the reliability of approximating Koopman spectra in nonlinear dynamical systems by automatically identifying optimal basis functions for the Koopman invariant subspace. The paper presents experiments on physical and biological systems, demonstrating the method's scalability and accuracy for complex dynamics.\n\nMy main comment on this paper is that it lacks sufficient innovation or fails to effectively demonstrate its unique contributions. The use of deep learning to estimate Koopman operators has been explored extensively in prior research. For example:\n\n[1] Lusch, B., Kutz, J. N., & Brunton, S. L. (2018). Deep learning for universal linear embeddings of nonlinear dynamics. Nature Communications, 9, Article 4950.\n[2] Mardt, A., Pasquali, L., Wu, H., & Noé, F. (2018). VAMPnets for deep learning of molecular kinetics. Nature Communications, 9, Article 5.\n[3] Mardt, A., Pasquali, L., Wu, H., & Noé, F. (2020). Deep learning Markov and Koopman models with physical constraints. Proceedings of Machine Learning Research, 107, 451-475.\n\nThe squared relative residual proposed in this paper has similarities to the VAMP-E score explored by Wu and Noé in their work on VAMP [4]. The VAMP score framework has served as a basis for many deep learning models, including VAMPnets [2], state-free reversible VAMPnets and GraphVAMPnets. It would be beneficial for the authors to position their spectral residual measure within this established framework, providing a comparative analysis or highlighting any differences in formulation or performance.\n\n[4] Wu, H., & Noé, F. (2020). Variational approach for learning Markov processes from time series data. Journal of Nonlinear Science, 30, 23-66." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "See the Summary" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "A collection of questions and suggestions have been made in the Weaknesses section." }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "- The description and clarity of the proposed approach is good overall.\n- The proposed approach is theoretically justified by the guarantees of the ResDMD method it is built upon.\n- The clustering results on the neural dynamics experiment are promising.\n- The code for the experiments is already available online." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a new method, NN-ResDMD, to estimate the spectral components of Koopman operators. It builds upon Residual Dynamic Mode Decomposition (ResDMD) and uses a neural network to identify basis functions instead of manually selecting them." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "**I do not think this paper makes a sufficiently strong contribution**. My understanding is that the proposed approach is simply ResDMD where the basis functions are parametrized by a neural network instead of specified manually, and then iteratively optimized. In my opinion this paper is better suited as a workshop paper in its current form.\n\nThe authors use feedforward neural network but the details of the architecture of the feedforward neural network are not provided. The authors **did not investigate the use and choice of other typically better-performing neural network architectures**. I don't think this should be left as future work but should already have been investigated. Similarly, exploring the direction of integrating the proposed approach with PINNs/PINOs would have made the contribution of the paper stronger, instead of leaving it as future work.\n\nThere is **no discussion of the computational cost** of the approach, which could be a big drawback of the proposed approach. As far as I understand it, the single evaluation (or few evaluations) of the eigenpairs in ResDMD is replaced by an iterative process where each iteration requires the evaluation of the eigenpairs in NN-ResDMD. This is an expensive part of the algorithm and there are no guarantees for the number of iterations required, so the running time there could be many times larger than for the classical ResDMD. In addition, there is also the cost of the additional optimization steps which can be very large if the neural networks are also large. Overall, the proposed algorithm seems significantly slower than existing approaches, so a trade-off between accuracy and computational time needs to be very carefully discussed theoretically and empirically.\n\nMore generally, the **limitations of the algorithm are not discussed**.\n\n**The results of the experiments are not clear**\n- The results of the pendulum experiment are not clear. Need to specify more precisely and explicitly what the ground truth is to understand if these are good/bad results. Why are the results of the NN-ResDMD approach shaded areas while all the other methods are displayed using points? The shaded area also has a large radius, so maybe the results are not as good as stated compared to Hankel-DMD for instance for which the points remain close to the unit circle.\n- The results of the turbulence experiments are not clear, since there is no ground truth provided. I am not familiar with that experiment so I do not know what the results are supposed to look like. It is unclear to me why the NN-ResDMD results in Figure 5 are considered good, while those in Figure 7 for Hankel-DMD are considered bad.\n- The results of the neural dynamics experiments could be made clearer. Why do you choose a different number of eigenfunctions for the different approaches? Is this a fair comparison? Figure 6.a., 9.a. and 10.a. show the decomposed eigenfunctions for the different approaches. There is no ground truth provided, so it is not clear to me what we learn from these plots.\n\nGiven that the proposed approach is compared to Hankel-DMD in all the numerical experiments, it would be worth detailing what that approach actually does compared to the other DMD approaches discussed in the paper. Maybe that was the original aim of Appendix A.5 which has been left empty.\n\nThe diagram in Figure 1 needs to be cleaner and more \"professional\"\n\nMake sure to specify the variables over which the optimization is performed in equations (3.4) and (3.5)" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "In Fig. 4, the spectrum of the estiated Koopman operator by NN-ResDMD is distributed on the unit circle. On the other hand, the spectra of the estimated Koopman operators by other methods are distributed also inside the unit circle. Is the dynamical system measure preserving, or did you only focus on the spectra on the unit circle?\n\nMinor comment: \nIn line 208, do you mean $(G+\\sigma I)^{-1}$ instead of $\\dagger$ since $G$ is positive-semi definite?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "ResDMD is a powerful tool to observe the spectra of Koopman operators. The authors combine neural networks and ResDMD to make ResDMD more flexible method. The topic is interesting and relevent to the community. The paper is well-organized and easy to follow." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors propose Neural Network-ResDMD (NN-ResDMD), where the dictionary functions are automatically selected by neural networks. The network is trained by minimizeing the loss function that is related to the residual of the eigenvalue problem of the Koopman operator. Numerical results are also illustrated to confirm the behavior of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "The authors insist their proposed method automatically select basis functions, eliminating the need for manual intervention. I understand that in practice, applying neural networks to find proper basis functions is more flexible. However, theoretically, advantages of applying neural networks to estimating Koopman operators and their eigenvalues are not clear for me. They assume there is a basis function $\\psi_i$ and construct a neural network that can sufficiently approximate $\\psi_i$. Could you clarify what is $\\psi_i$? And, what impact on estimating Koopman operators and their eigenvalues is induced by the error of the approximation of $\\psi_i$ by using the neural network?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024nnresdmd,\ntitle={{NN}-Res{DMD}: Learning Koopman Representations for Complex Dynamics with Spectral Residuals},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=53xxT3LwJB},\nnote={under review}\n}" }, "abstract": { "value": "Analyzing long-term behaviors in high-dimensional nonlinear dynamical systems remains a significant challenge. The Koopman operator framework has emerged as a powerful tool to address this issue by providing a globally linear perspective on nonlinear dynamics. However, existing methods for approximating the Koopman operator and its spectral components, particularly in large-scale systems, often lack robust theoretical guarantees.\nResidual Dynamic Mode Decomposition (ResDMD) introduces a spectral residual measure to assess the convergence of the estimated Koopman spectrum, which helps filter out spurious spectral components. \nNevertheless, it depends on pre-computed spectra, thereby inheriting their inaccuracies. \nTo overcome its limitations, we introduce the Neural Network-ResDMD (NN-ResDMD), a method that directly estimates Koopman spectral components by minimizing the spectral residual. By leveraging neural networks, NN-ResDMD automatically identifies the optimal basis functions of the Koopman invariant subspace, eliminating the need for manual selection and improving the reliability of the analysis.\nExperiments on physical and biological systems demonstrate that NN-ResDMD significantly improves both accuracy and scalability, making it an effective tool for analyzing complex dynamical systems." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Koopman operator", "data driven dynamical system", "dynamic mode decomposition" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/3cd2cd49252dd7db8627463a2090a1dfb6f8179d.pdf" }, "presentation": null, "primary_area": { "value": "learning on time series and dynamical systems" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/b79d28d3e6ec81b18e9bc30b118e6597ffe5f858.zip" }, "title": { "value": "NN-ResDMD: Learning Koopman Representations for Complex Dynamics with Spectral Residuals" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "1. If I understand correctly, for the downstream tasks, the evaluation metric used is perplexity. Why is perplexity chosen as the metric instead of one that is specific to the dataset or task itself?\n2. Is there any potential explanation for why AutoScale doesn't perform as well on encoder-only models compared to decoder-only models?" }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. AutoScale presents an interesting idea that distinguishes it from previous work, demonstrating that the optimal weights are only effective at the scale they were optimized for and become suboptimal when applied to other scales. It offers a practical method for automatically and efficiently determining domain weights when train large language models. \n2. The experiments are conducted on both encoder-only and decoder-only models and shows good results on decoder-only model. \n3. The work is supported by both empirical experiments and mathematical formulations. Additionally, the diagram in the paper is well-designed and effectively conveys the underlying concepts." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "The authors address an interesting topic in this paper: a method for automatically optimizing the mixture proportions of pretraining data domains when training language models. \nThey begin by formulating the optimal mixing problem as a bi-level optimization and then propose the Direct Data Optimization (DDO) algorithm to formalize the relationship between optimal data compositions and training data scales. Using DDO, they conduct empirical studies to optimize domain weights at various training data scales, demonstrating that the optimal data composition varies with the scale of the training data. Finally, they introduce AutoScale, which automatically predicts optimal training data compositions at larger scales based on compositions optimized at smaller scales. \nAdditionally, their evaluation of AutoScale on both decoder-only and encoder-only models demonstrates its ability to achieve computational savings." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "1. The experimental setup is not entirely convincing:\n\n* The models used (a 774M decoder-only model and a 110M encoder-only model) are relatively small compared to today’s large language models, making it difficult to gauge performance at a larger scale.\n* The data size is limited to 3B, 5B, and 10B tokens, with results in Table 1 only reflecting the 3B set.\n* Figure 3(b) lacks explanation, and the cola baseline and DDO performance seems unusually low, falling below random guessing (0.5). Also, stsb baseline seems low too.\n\n2. The evaluation of downstream tasks could be expanded. It would be helpful to see the models' performance on more complex tasks, such as mathematical problem-solving." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "- I am a bit unclear about your definition of \"equivalent data size\" at L243, what's the equivalence about (i.e., which size and which size)? Note that I understand the meaning of $N_I^0$, just wondering the terminology here.\n- Maybe I missed something, but how do one control the budget for the next $N^(3)$? It seems the amount of tokens is defined by the initial weights of $N^(1)$ and $N^(2)$. Or in other words, say I need to find a optimal weight for a total token of 300B, how should I start with $N^(1)$ and $N^(2)$?\n- Adding to the prior question, if the optimal ratio of each domain follows a exponential function, after taking a few data points using AutoScale, can we simply fit the exponential function instead of using the AutoScale iterative method? You seem to be using that in Figure 1 (d). If y es, this simply answer my question above.\n- While the problem of different data scale is resolved with a scaling law solution, can we also use a similar approach on model scale? Even though the cost of using a small amount of data for a larger model should be within a low percentage of the total training cost, setting up the experiment for the larger scale is non-trivial. It'd be nice to have a function that can predict the loss across model scales." }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "- This paper analyzes an important problem, data weighting of LLM training, which can improve the training efficiency with reasonable cost. It also presents an actionable algorithm for LLM training.\n- The proposed method assumes a power law formulation which makes the data weighting problem practically solvable. It is important to point out that data weights is scale dependent.\n- The empirical results and findings on the corpus weighting align with common belief of the community, such that further up-weighting high quality source is less effective, and books and web documents continue to be important at larger scale. This shows that the proposed method has strong explanatory ability.\n- The experiment is quite thorough, considering the cost for training models is quite high even at small scales." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper studies the problem of predicting optimal data mix for a given compute budget (i.e., fixed total token count and model size). A key challenge here is that the optimal domain weighting may change at different scale, hence it is inaccurate to use smaller models to predict large model performance, while solving the optimization problem at the large model scale directly is computationally infeasible (requires multiple retraining).\n\nThe paper proposes a method that work on one domain at a time by fixing the rest of the data constant (hence the loss is constant for other domains too), and estimated a scaling law per domain. The power law parameters $\\gamma_i$ and $l_i$ can be easily estimated, which approximate a regular data scaling function where $l_i$ is the irreducible loss of that domain. \n\nAfter the power law of each domain is found, the final objective is to mix the data so that the loss is minimize while keeping sum of the tokens reaches the budget, which becomes a convex function that can be solved efficiently. This gives the DDO method. The different $\\gamma_i$ explains why there is a differet mix at different stage.\n\nA method \"AutoScale\" is further proposed to obtain the data weight of a larger token budget, by iteratively mxing two data weights at different scale to create the weights of the next one. \n\nThe proposed approach is tested on models like GPT-2 (autoregressive) and BERT (bidirectional), showing improved convergence rates and downstream task performance. Empricially, the results show AutoScale’s ability to shift data weights, favoring diverse data sources like CommonCrawl at larger scales while reducing reliance on traditionally high-quality, standard-format data such as Wikipedia. These findings match the empricial findings of the data weights used for prior succesful models such as Llama." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "- I wonder if carbon footprint of the experiments here should also be reported\n- The presentation is good but can still be improved. The core method part can be improved with additional intuitive explanations and better use of notations. Further, I have noted down some minor typo/notation errors:\n\nTypo or notation:\n - L258: $N_i^+2$ should probably be $N_i^+$\n - L526: Ecoder -> Encoder \n - L200: probably should better use $N_i$ instead of $|S_i|$\n - L281: $w_i'*N'$ appears twice.\n - Figure 2 caption: meaning of (original = 1) is a bit unclear\n - L380: $N$ is missing in the first equation." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 2 }, "primary_area": null, "questions": { "value": "Fig 1: [a] suggests that you've tuned 6 models between 30M and 1.2 B tokens, yet [c] shows only three models being used to fit the predictor model. Why is that? where are the other data points? And are *all* of the linear fits R2=0.998? Is that the average R2? Also, [d] shows the predictions of the model extrapolated past 1.2 B to 307 B? Why are you not showing the training data points (between 30M and 1.2 B) as well? And isn't the largest model you look at trained to 10B? why show this extrapolation to so far beyond where you explore? This seems misleading. The x-axis should say (log scale) as well. In [b] the color used for the 1.2B model is the color used for the 0.6B model in [a]. And there is a typo in the title ('scale - depedent' -> \"dependent\"). In [e] the 38% improvement looks to be overstated due to the noise of those evaluation curves, you could just as easily pick out the peak in Autoscale curve at step 86k and the point in the Uniform curve at step 100k to get a smaller improvment result with the same underlying data. \n\nTable 1: boolq has the Autoscale value bolded as 'best' but the Data Mixing Laws value is greater. Also, consider place your method on the bottom row separated by a thin line.\n\nFig2: What is being depicted here? Is this showing power laws being fit to 3 empirical datapoints? Is the first column of points supposed to be at 0? It looks like the points are at [0.2, 1, 3] on the x-axis? \n\n\nNits:\n\nThroughout: \"AutoScale\" is consistently the wrong font size. Please fix. Similarly, in section 5.2 the font size of the methods needs to be fixed. And in line 418 'from' is included in the method font instead of the default font.\n181: work contribute -> contributes\n379: N^(1)* is missing the N in summation\n465: much lowered -> much lower\n155 'a consistent shift can be observed', please be more specific, what is shifting, how is it consistent?" }, "rating": { "value": 3 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "The general problem the authors attempt to address is important, and the assessment that present methods are limited and that performance headroom is available is well-framed. The code is open-sourced. The evaluations presented are limited, but positive." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work presents a method to estimate optimal data domain weights at large training-data scales by extrapolating via exponential functions fit to smaller-scale training runs. The proposed method is evaluated on GRT-2 Large + RedPajama and BERT pretraining, and compared to extant method baselines." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "In general, the writing is difficult to parse. It is frequently frustratingly vague, including in the Abstract, where the actual method is alluded to but not elucidated. In the actual methods section, important questions about the method are unanswered, leaving the method underspecified (is the learning rate schedule (linear? presumably linear decay?) the same for the tuning runs as for the final run? Is the decay timing adjusted to the compute budget? The value of the final validation loss hinges critically on this - yet it goes unmentioned). There is no addressing of the profound difficulties this method (and others like it) can be expected to have around epochs for individual datasets. A more thorough analysis would identify and investigate this issue with experiments demonstrating specific datasets being sampled for > 1 epoch, and the subsequent breakdown of the \"scaling law\" prediction. Evalutation is purely comparative to other methods, and does not assess to what extent the predicted 'optimal' values might differ from more expensively traditionally-derived 'optimal' values. No discussion of the relative cost of the method (with its 'linearly scaling' cost in the number of datasets) is mentioned, though it is clear it would become prohibitively expensive for dataset mixtures with more than a handful of individual datasets. The method proposed is prohibitively expensive at large model sizes, and seems unlikely to scale to larger compute budgets even at small dataset sizes due to the issue of datasets passing through multiple epochs, which is unaddressed in this work. This limitation goes unmentioned." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "* Even at a smaller scale, I see opportunities of clear promise where we could have had more points between 0.3B and 1.2B and show some trend. Any specific reason this was not done/ increased to more than 1.2B ? With scale, a lot of problems disappear that are apparent at lower scales." }, "rating": { "value": 5 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "* Very strong work in terms of the hypothesis and experimental setup albeit at smaller scales. The promise of finding optimal weights for training large networks without having to guesstimate it is a very attractive proposition. \n* The plots are really well done. They drive the main idea of the paper very well(especially Fig 1 (a, e) )" }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This work proposes a method called “AutoScale” that helps predict the optimal composition of pre-training data for LLMs. It challenges the conventional notion of determining this via small scale experiments and simply applying them to a large scale where two axes change (data scale, parameter count). The experiments show a very promising line of research and it was a pleasure to read. \n\nI couldn’t check the math as well as I would have liked to." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "I would like to list the following weakness fully ensuring the authors that I am not unreasonable and am completely open to increasing my score if these are addressed/answered satisfactorily.\n* The work proposes using a different approach to finding optimal data weights for a given pre-training compute budget. This is well explained via results but does in fact require training the original size model. Given that we obtain suboptimal performance via the conventional way( smaller model, fewer data), an analysis showing how much performance could be gained by spending the compute and training these (equal parameter) networks would be useful. \n* For Takeaway 1, Fig 1(b) only has 2 data points. Additional points would help make the case stronger. It’s a tough sell to make such a bold statement with two data points. But I’m hoping I am wrong :) \n* Maybe I missed this, but the repeated claims that Wikipedia should be sampled less at a higher scale is a result of the OLS fit. But no experiment actually confirmed this fact in the paper, right ? Since the max scale was 1.2B ? Please correct me if I’m wrong.\n\nGeneral Comments/Typos:\n* [Section2] : “this work contribute” -> “this work contributes”\n* [Section 3.1] : wi = Ni/N => wi = Si/N ?\n* [Algorithm 1] : Train the model on data S = ({S1 . . . Sm} \\ Si) => S = ({S1 . . . Sm} \\ Sj) ? \n* Some of the font sizes are very distracting to read." }, "withdrawal_confirmation": null }, { "TLDR": { "value": "We propose AutoScale, which automatically predicts compute-optimal data compositions for training LLMs at the target training data scale." }, "_bibtex": { "value": "@inproceedings{\nanonymous2024autoscale,\ntitle={AutoScale: Automatic Prediction of Compute-optimal Data Compositions for Training {LLM}s},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=54KcduuYeG},\nnote={under review}\n}" }, "abstract": { "value": "Domain reweighting is an emerging research area aimed at adjusting the relative weights of different data sources to improve the effectiveness and efficiency of language model pre-training. This paper demonstrates that the optimal composition of training data from different domains is scale-dependent, challenging the existing practice of determining optimal mixtures through small-scale experiments and directly applying them at larger scales. We derive an analytical model for the dependence of optimal weights on data scale and introduce *AutoScale*, a novel, practical approach for optimizing data compositions at potentially large training data scales. *AutoScale* first uses a principled optimization framework to find optimal compositions at smaller, feasible scales, then predicts optimal compositions at larger scales using our derived model. Our evaluation on GPT-2 Large and BERT pre-training demonstrates *AutoScale*'s effectiveness in improving training convergence and downstream performance. Particularly, for GPT-2 Large on RedPajama, *AutoScale* decreases validation perplexity 28% faster than baselines, with up to 38% speed-up over unweighted training, achieving the best performance across downstream tasks. This work provides insights into the varying benefits of data sources across training scales for language models, contributing to the burgeoning research on scale-dependent data curation. Code is open-sourced" }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Data Curation", "Data Composition", "Scaling Laws", "Data-centric AI", "Large Language Models (LLM)" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/39bf08753f3b74d26b72f7d4723fb0ebbcd5b1dc.pdf" }, "presentation": null, "primary_area": { "value": "foundation or frontier models, including LLMs" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/61c26777f8a43234e782dd6eec90b596cb6d9c7f.pdf" }, "title": { "value": "AutoScale: Automatic Prediction of Compute-optimal Data Compositions for Training LLMs" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]

[ { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 1 }, "contribution": { "value": 2 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "No questions." }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 2 }, "strengths": { "value": "1. **Bandwidth Efficiency**: The proposed \"point cluster\" message unit significantly reduces communication bandwidth by capturing only essential foreground object information in a sparse format, making it highly efficient compared to dense representations like raw point clouds and BEV maps.\n\n2. **Enhanced Object Detection Accuracy**: By preserving detailed structural and semantic information, the point cluster improves object detection accuracy, especially in complex multi-agent scenarios, demonstrating superior performance on established collaborative perception benchmarks.\n\n3. **Robustness to Real-world Challenges**: The CPPC framework includes robust mechanisms for handling pose errors and time delays, crucial for real-world applications. Parameter-free solutions for pose and latency issues make it adaptable to various levels of noise without additional tuning.\n\n4. **Clear and Effective Writing**: The paper is well-written, with a clear explanation of the proposed methods and thorough descriptions of experiments, which makes the complex technical content accessible and supports the credibility and reproducibility of the research findings." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper presents a new message unit, the \"point cluster,\" to improve collaborative perception efficiency in multi-agent systems. Unlike existing message units such as raw point clouds, bounding boxes, or BEV maps, the point cluster format minimizes bandwidth usage while retaining essential structural and semantic information. Representing objects with point coordinates, a cluster center, and semantic features, this approach allows efficient inter-agent information exchange, enhances object alignment, and preserves object structure for more accurate detection. A new framework, CPPC, combines point packing and aggregation modules, addressing issues like bandwidth constraints, time delay, and pose errors, and achieves state-of-the-art performance on several benchmarks." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "In terms of methodology, there is a noticeable reliance on FSD, which slightly reduces the originality. However, overall, the approach is still reasonable." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 4 }, "contribution": { "value": 4 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "If we assume some objects are moving at high speeds, I think the errors caused by delay would vary for each object. It seems this aspect might not be covered, does the proposed method handle this problem?" }, "rating": { "value": 8 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "This paper systematically analyzes the advantages and disadvantages of existing collaborative perception methods (early, intermediate, late collaboration) and proposes a rational solution considering the inherent trade-off between communication efficiency and perception performance. \nThe proposed point cluster is a compact message unit that selectively combines the strengths of existing methods and efficiently represents both structural and semantic information of objects. \nCPPC introduces parameter-free approaches to handle pose error and time latency issues encountered in real-world scenarios, ensuring robustness to various noise levels. \nThis paper clearly identifies the current challenges and limitations in the field of collaborative perception and demonstrates how the proposed method can rationally solve them.\nThrough clear problem formulation, the collaborative perception problem is mathematically defined, and based on this, the motivation and principles of the proposed method are lucidly explained." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes a novel message exchange unit called point cluster for collaborative perception. Point clusters can efficiently and compactly represent an object's location, structure, and semantic information. The proposed CPPC framework includes point cluster-based encoding, packing, exchange, and integration methods. CPPC improves both communication efficiency and perception accuracy while being robust to various real-world noises. Experimental results on various benchmarks demonstrate that CPPC significantly outperforms existing methods." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "Based on the proposed technique, the amount of data that can be transmitted is limited. Therefore, it is crucial to prioritize foreground objects and bring them in a sparse manner to distinguish the overall shape, rather than bringing only a few parts. Regarding this aspect, I wonder if there are any criteria or tendencies for determining which points should be selected and what rules should be followed. Additionally, I am curious about the extent of performance degradation when background information is included instead of solely focusing on the foreground. \n\nIf we follow the logic mentioned above, I wonder if we can expect sufficient performance improvement in early or late collaboration by only bringing foreground information and adjusting it according to the available bandwidth. \n\n- In Figure 4(a), there is a section where the performance of the proposed method rises sharply. I am curious about the reason behind this phenomenon and how the performance of the proposed method would differ from existing technologies if the communication volume is lower than this point. \n- On Page 7, Line 373, it is mentioned that the proposed method shows improvements of 5.7%, 7.3%, and 12.8%, but it is unclear which methods are being compared. \n- On Page 7, Line 377, the term \"late collaboration\" is used. I am curious about which specific technique this refers to." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 3 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 4 }, "primary_area": null, "questions": { "value": "My current concerns are mainly about the technical contributions of the proposed method. I may change my rating after reading other reviewers' comments and the authors' rebuttal. Here are some questions I wish could be addressed:\n\nQ1: In the proposed SD-FPS method, how to balance the weights of semantic and spatial distances ($\\lambda_s$ and $\\lambda_d$)? How will they affect the performance of the proposed method?\n\nQ2: Does the proposed method perform well in a crowded environment where many objects are exhibited?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 4 }, "strengths": { "value": "\\+ This paper is written well and the motivation behind the proposed method is easy to follow.\n\n\\+ The proposed method is a systematic solution for multi-agent perception, which exploits point clusters as the intermediate representation with controllable communication costs, to balance efficiency and effectiveness. \n\n\\+ The proposed method outperforms several previous methods on three public datasets consistently. The extensive experimental results validate the effectiveness of the proposed modules." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper introduces a communication-efficient collaborative perception (CPPC) framework for vehicle-to-everything autonomous driving. Unlike previous methods that mainly rely on BEV features, the CPPC framework leverages point clusters to control its computational complexity and alleviate issues like high-level information loss. Specifically, the CPPC framework consists of a point cluster picking module, a pose alignment and latency compensation module, and a point cluster aggregation module to generate cluster features for subsequent predictions. The CPPC framework outperforms existing BEV-based methods on three public datasets, including V2XSet, OPV2V, and DAIR-V2X-C." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "\\- There are four thresholds used in the proposed method, which may be hard to choose in complex or unseen scenarios.\n\n\\- The proposed method mainly considers BEV-based methods for comparisons, however, it is unclear how significant its technical contributions are compared with previous point-cloud-based methods (e.g., Cooper, F-cooper). I am concerned about this because the techniques used in the main components of the proposed method, including point cluster picking, pose correction, and the SD-FPS, are quite common in the research communities of point cloud processing." }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": null, "abstract": null, "anonymous_url": null, "authorids": null, "authors": null, "code_of_conduct": { "value": "Yes" }, "code_of_ethics": null, "comment": null, "confidence": { "value": 2 }, "contribution": { "value": 3 }, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": { "value": [ "No ethics review needed." ] }, "keywords": null, "large_language_models": null, "no_acknowledgement_section": null, "other_comments_on_LLMs": null, "paperhash": null, "pdf": null, "presentation": { "value": 3 }, "primary_area": null, "questions": { "value": "In section 3.5, can the authors detail \"spatial cluster matching\"? If the pose between two agents is not accurate, how to match point clusters from different agents into a single object?" }, "rating": { "value": 6 }, "reciprocal_reviewing": null, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": { "value": 3 }, "strengths": { "value": "1. Using point cluster as an intermediate message unit is novel and well-motivated.\n2. The proposed CPPC framework, as well as the PCE, PCP and PCA modules are solid.\n3. State-of-the-art performance on mainstream benchmarks." }, "student_author": null, "submission_guidelines": null, "summary": { "value": "This paper proposes to use Point Cluster as the message unit for collaborative perception. Previous intermediate methods use BEV map as the message unit, which suffer from weak object features, inefficient message aggregation and vague boundary. The proposed point cluster-based framework can solve these problems well. Extensive experiments on V2XSet, OPV2V, and DAIR-V2X-C demonstrate the effectiveness of the proposed method." }, "supplementary_material": null, "title": null, "venue": null, "venueid": null, "weaknesses": { "value": "My main concerns are about the effectiveness and efficiency of the proposed method, for which I think more ablation studies are required.\n1. Ablation studies on PCP, PCA, pose correction and latency compensation are required.\n2. The authors claim the BEV representation is inefficient during aggregation. Is there any comparison between BEV and Point Cluster-based methods?" }, "withdrawal_confirmation": null }, { "TLDR": null, "_bibtex": { "value": "@inproceedings{\nanonymous2024point,\ntitle={Point Cluster: A Compact Message Unit for Communication-Efficient Collaborative Perception},\nauthor={Anonymous},\nbooktitle={Submitted to The Thirteenth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=54XlM8Clkg},\nnote={under review}\n}" }, "abstract": { "value": "The objective of the collaborative perception task is to enhance the individual agent's perception capability through message communication among neighboring agents. A central challenge lies in optimizing the inherent trade-off between perception ability and communication cost. To tackle this bottleneck issue, we argue that a good message unit should encapsulate both semantic and structural information in a sparse format, a feature not present in prior approaches. In this paper, we innovatively propose a compact message unit, namely point cluster, whose core idea is to represent potential objects efficiently with explicitly decoupled low-level structure information and high-level semantic information. Building upon this new message unit, we propose a comprehensive framework CPPC for communication-efficient collaborative perception. The core principle of CPPC is twofold: first, through strategical point sampling, structure information can be well preserved with a few key points, which can significantly reduce communication cost; second, the sequence format of point clusters enables efficient message aggregation by set matching and merging, thereby eliminating unnecessary computation generated when aligning squared BEV maps, especially for long-range collaboration. To handle time latency and pose errors encountered in real-world scenarios, we also carefully design parameter-free solutions that can adapt to different noisy levels without finetuning. Experiments on two widely recognized collaborative perception benchmarks showcase the superior performance of our method compared to the previous state-of-the-art approaches." }, "anonymous_url": { "value": "I certify that there is no URL (e.g., github page) that could be used to find authors’ identity." }, "authorids": null, "authors": null, "code_of_conduct": null, "code_of_ethics": { "value": "I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics." }, "comment": null, "confidence": null, "contribution": null, "desk_reject_comments": null, "details_of_ethics_concerns": null, "flag_for_ethics_review": null, "keywords": { "value": [ "Point Cluster", "Collaborative Perception" ] }, "large_language_models": null, "no_acknowledgement_section": { "value": "I certify that there is no acknowledgement section in this submission for double blind review." }, "other_comments_on_LLMs": null, "paperhash": null, "pdf": { "value": "/pdf/a707dc6cc0d9f85f9b3b3daa8da754cb500e3e36.pdf" }, "presentation": null, "primary_area": { "value": "applications to computer vision, audio, language, and other modalities" }, "questions": null, "rating": null, "reciprocal_reviewing": { "value": "I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6." }, "resubmission": null, "revert_desk_rejection_confirmation": null, "revert_withdrawal_confirmation": null, "soundness": null, "strengths": null, "student_author": null, "submission_guidelines": { "value": "I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide." }, "summary": null, "supplementary_material": { "value": "/attachment/e2e36c951a5a727fdad79b13ce043dc1fb56bbcc.zip" }, "title": { "value": "Point Cluster: A Compact Message Unit for Communication-Efficient Collaborative Perception" }, "venue": { "value": "ICLR 2025 Conference Submission" }, "venueid": { "value": "ICLR.cc/2025/Conference/Submission" }, "weaknesses": null, "withdrawal_confirmation": null } ]