@inproceedings{
chen2024learning,
title={Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor},
author={Jiali Chen and Xusen Hei and Yuqi Xue and Yuancheng Wei and Jiayuan Xie and Yi Cai and Qing Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=HDM47gpvq6}
}
Large multimodal models (LMMs) have shown remarkable performance in the visual commonsense reasoning (VCR) task, which aims to answer a multiple-choice question based on visual commonsense within an image. However, the ability of LMMs to correct potential visual commonsense errors in the distractor upon their occurrence is yet under-explored. Drawing inspiration from how a human teacher crafts challenging distractors to test students' comprehension of the concepts or skills and assists them in identifying and correcting errors toward the answer, we are the pioneering research for LMMs to simulate this error correction learning process. To this end, we employ GPT-4 as a ``teacher'' to collect the explainable feedback dataset VCR-DF for error correction, which serves as a benchmark to evaluate the ability of LMMs to identify misconceptions and clarify reasons behind the error in VCR distractors toward final answers. In addition, we propose an LMM-based Pedagogical Expert Instructed Feedback Generation (PEIFG) model to incorporate the learnable expert prompts and multimodal instruction as guidance for feedback generation. Experimental results show that our PEIFG significantly outperforms existing LMMs. We believe our benchmark carves out a new direction for evaluating the capabilities of LMMs.
Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor
Jiali Chen Xusen Hei Yuqi Xue Yuancheng Wei Jiayuan Xie Yi Cai Qing Li