stereoplegic
's Collections
Diversity of Thought Improves Reasoning Abilities of Large Language
Models
Paper
•
2310.07088
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Published
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5
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning
Paper
•
2310.04474
•
Published
•
2
Promptor: A Conversational and Autonomous Prompt Generation Agent for
Intelligent Text Entry Techniques
Paper
•
2310.08101
•
Published
•
2
Instance Needs More Care: Rewriting Prompts for Instances Yields Better
Zero-Shot Performance
Paper
•
2310.02107
•
Published
•
3
AskIt: Unified Programming Interface for Programming with Large Language
Models
Paper
•
2308.15645
•
Published
•
2
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Paper
•
2305.10601
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Published
•
11
From Sparse to Dense: GPT-4 Summarization with Chain of Density
Prompting
Paper
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2309.04269
•
Published
•
32
FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Paper
•
2309.04663
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Published
•
5
Large Language Models Are Also Good Prototypical Commonsense Reasoners
Paper
•
2309.13165
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Published
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1
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM
Inference with Transferable Prompt
Paper
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2305.11186
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Published
•
1
Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from
Knowledge Graphs
Paper
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2309.03118
•
Published
•
2
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large
Language Models
Paper
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2308.09729
•
Published
•
5
A Unified Generative Retriever for Knowledge-Intensive Language Tasks
via Prompt Learning
Paper
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2304.14856
•
Published
•
1
Prompt Engineering and Calibration for Zero-Shot Commonsense Reasoning
Paper
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2304.06962
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Published
•
1
Efficient Prompting via Dynamic In-Context Learning
Paper
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2305.11170
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Published
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1
Adapting Language Models to Compress Contexts
Paper
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2305.14788
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Published
•
1
Paper
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2203.12119
•
Published
•
1
Do We Really Need a Large Number of Visual Prompts?
Paper
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2305.17223
•
Published
•
1
Better Zero-Shot Reasoning with Role-Play Prompting
Paper
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2308.07702
•
Published
•
2
Scaled Prompt-Tuning for Few-Shot Natural Language Generation
Paper
•
2309.06759
•
Published
•
1
Terminology-Aware Translation with Constrained Decoding and Large
Language Model Prompting
Paper
•
2310.05824
•
Published
•
1
Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language
Models
Paper
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2307.08303
•
Published
•
1
Discrete Prompt Optimization via Constrained Generation for Zero-shot
Re-ranker
Paper
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2305.13729
•
Published
•
1
Context Aware Query Rewriting for Text Rankers using LLM
Paper
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2308.16753
•
Published
•
1
Soft-prompt Tuning for Large Language Models to Evaluate Bias
Paper
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2306.04735
•
Published
•
1
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural
Language Understanding
Paper
•
2306.04933
•
Published
•
1
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization
for Few-shot Generalization
Paper
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2303.12314
•
Published
•
1
Contrastive Learning for Prompt-Based Few-Shot Language Learners
Paper
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2205.01308
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Published
•
1
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive
Prompt-Based Few-Shot Fine-Tuning
Paper
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2305.18169
•
Published
•
1
Pre-training with Large Language Model-based Document Expansion for
Dense Passage Retrieval
Paper
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2308.08285
•
Published
•
1
Privacy-Preserving Prompt Tuning for Large Language Model Services
Paper
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2305.06212
•
Published
•
1
Tuning Language Models as Training Data Generators for
Augmentation-Enhanced Few-Shot Learning
Paper
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2211.03044
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Published
•
1
ConsPrompt: Easily Exploiting Contrastive Samples for Few-shot Prompt
Learning
Paper
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2211.04118
•
Published
•
1
Contrastive Demonstration Tuning for Pre-trained Language Models
Paper
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2204.04392
•
Published
•
1
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large
Language Models
Paper
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2305.18507
•
Published
•
1
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
Paper
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2306.06427
•
Published
•
2
Progressive-Hint Prompting Improves Reasoning in Large Language Models
Paper
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2304.09797
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Published
•
1
Small Language Models Improve Giants by Rewriting Their Outputs
Paper
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2305.13514
•
Published
•
2
Introspective Tips: Large Language Model for In-Context Decision Making
Paper
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2305.11598
•
Published
•
1
Program of Thoughts Prompting: Disentangling Computation from Reasoning
for Numerical Reasoning Tasks
Paper
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2211.12588
•
Published
•
3
Improving ChatGPT Prompt for Code Generation
Paper
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2305.08360
•
Published
•
1
Not All Languages Are Created Equal in LLMs: Improving Multilingual
Capability by Cross-Lingual-Thought Prompting
Paper
•
2305.07004
•
Published
•
1
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for
Code Generation
Paper
•
2310.10698
•
Published
•
1
Test-Case-Driven Programming Understanding in Large Language Models for
Better Code Generation
Paper
•
2309.16120
•
Published
•
1
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot
Learners
Paper
•
2110.06274
•
Published
•
1
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization
for Relation Extraction
Paper
•
2104.07650
•
Published
•
2
Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
Paper
•
2305.01711
•
Published
•
1
Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual
Understanding With Multilingual Language Models
Paper
•
2210.12360
•
Published
•
1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Paper
•
2309.05173
•
Published
•
1
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than
In-Context Learning
Paper
•
2205.05638
•
Published
•
3
Connecting Large Language Models with Evolutionary Algorithms Yields
Powerful Prompt Optimizers
Paper
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2309.08532
•
Published
•
52
Automatic Prompt Optimization with "Gradient Descent" and Beam Search
Paper
•
2305.03495
•
Published
•
1
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Paper
•
2307.15780
•
Published
•
25
XPrompt: Exploring the Extreme of Prompt Tuning
Paper
•
2210.04457
•
Published
•
1
Paper
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2103.10385
•
Published
•
8
Reasoning with Language Model Prompting: A Survey
Paper
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2212.09597
•
Published
•
2
Automatic Chain of Thought Prompting in Large Language Models
Paper
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2210.03493
•
Published
•
2
Prompt Space Optimizing Few-shot Reasoning Success with Large Language
Models
Paper
•
2306.03799
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Published
•
1
Large Language Models are Better Reasoners with Self-Verification
Paper
•
2212.09561
•
Published
•
1
Repository-Level Prompt Generation for Large Language Models of Code
Paper
•
2206.12839
•
Published
•
2
Enhancing Automated Program Repair through Fine-tuning and Prompt
Engineering
Paper
•
2304.07840
•
Published
•
1
Prompt Engineering or Fine Tuning: An Empirical Assessment of Large
Language Models in Automated Software Engineering Tasks
Paper
•
2310.10508
•
Published
•
1
Beyond Words: A Mathematical Framework for Interpreting Large Language
Models
Paper
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2311.03033
•
Published
•
1
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought
Generation
Paper
•
2311.04254
•
Published
•
13
Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
Paper
•
2305.03268
•
Published
•
2
Prompt Sketching for Large Language Models
Paper
•
2311.04954
•
Published
•
2
Exploring the Intersection of Large Language Models and Agent-Based
Modeling via Prompt Engineering
Paper
•
2308.07411
•
Published
•
2
CODA-Prompt: COntinual Decomposed Attention-based Prompting for
Rehearsal-Free Continual Learning
Paper
•
2211.13218
•
Published
•
1
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
Paper
•
2308.10445
•
Published
•
1
Unleashing Cognitive Synergy in Large Language Models: A Task-Solving
Agent through Multi-Persona Self-Collaboration
Paper
•
2307.05300
•
Published
•
18
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly
Generating Predictions and Natural Language Explanations
Paper
•
2305.13235
•
Published
•
1
Prompt Engineering a Prompt Engineer
Paper
•
2311.05661
•
Published
•
20
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
Paper
•
2208.08340
•
Published
•
1
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene
Classification
Paper
•
2309.09276
•
Published
•
1
Approximated Prompt Tuning for Vision-Language Pre-trained Models
Paper
•
2306.15706
•
Published
•
1
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Paper
•
2304.09402
•
Published
•
2
Prompting with Pseudo-Code Instructions
Paper
•
2305.11790
•
Published
•
2
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
Paper
•
2310.02304
•
Published
•
1
Cognitive Architectures for Language Agents
Paper
•
2309.02427
•
Published
•
8
Contrastive Chain-of-Thought Prompting
Paper
•
2311.09277
•
Published
•
34
Flows: Building Blocks of Reasoning and Collaborating AI
Paper
•
2308.01285
•
Published
•
2
The Impact of Positional Encoding on Length Generalization in
Transformers
Paper
•
2305.19466
•
Published
•
2
OpenPrompt: An Open-source Framework for Prompt-learning
Paper
•
2111.01998
•
Published
•
1
Source Prompt: Coordinated Pre-training of Language Models on Diverse
Corpora from Multiple Sources
Paper
•
2311.09732
•
Published
•
1
Principled Instructions Are All You Need for Questioning LLaMA-1/2,
GPT-3.5/4
Paper
•
2312.16171
•
Published
•
34
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for
Text Entry: A Case Study on Abbreviation Expansion
Paper
•
2312.14327
•
Published
•
6
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image
Diffusion Models with Large Language Models
Paper
•
2305.13655
•
Published
•
7
Mixture of Soft Prompts for Controllable Data Generation
Paper
•
2303.01580
•
Published
•
1
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning
Paper
•
2305.18170
•
Published
•
2
Large Language Models Are Human-Level Prompt Engineers
Paper
•
2211.01910
•
Published
•
1
Metacognitive Prompting Improves Understanding in Large Language Models
Paper
•
2308.05342
•
Published
•
2
Graph Prompt Learning: A Comprehensive Survey and Beyond
Paper
•
2311.16534
•
Published