Summary of Learning to Correction: Explainable Feedback Generation For Visual Commonsense Reasoning Distractor, by Jiali Chen et al.
Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor
by Jiali Chen, Xusen Hei, Yuqi Xue, Yuancheng Wei, Jiayuan Xie, Yi Cai, Qing Li
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large multimodal models have achieved remarkable performance in visual commonsense reasoning tasks, but their ability to correct potential errors in distractors is under-explored. To address this, we developed a pioneering research approach that simulates error correction processes, inspired by how human teachers craft challenging distractors and assist students in identifying and correcting errors. We employed GPT-4 as a “teacher” to collect the explainable feedback dataset VCR-DF for error correction, serving as a benchmark to evaluate LMMs’ ability to identify misconceptions and clarify reasons behind errors. Our PEIFG model incorporates learnable expert prompts and multimodal instruction as guidance for feedback generation, significantly outperforming existing LMMs in experimental results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large multimodal models are really good at answering questions based on images. But they’re not great at fixing mistakes when they make them. We wanted to change that. We came up with a new way for LMMs to learn from their mistakes, inspired by how teachers help students understand where they went wrong. We used a powerful language model as our “teacher” to collect a special dataset of feedback and corrections. This helps us test how well LMMs can identify and fix mistakes in the first place. Our new approach is called PEIFG, and it does a much better job than previous models at giving good advice. |
Keywords
» Artificial intelligence » Gpt » Language model
