Summary of Vision-language Models Can Self-improve Reasoning Via Reflection, by Kanzhi Cheng et al.
Vision-Language Models Can Self-Improve Reasoning via Reflection
by Kanzhi Cheng, Yantao Li, Fangzhi Xu, Jianbing Zhang, Hao Zhou, Yang Liu
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel self-training framework called R3V is proposed to enhance the vision-language reasoning capabilities of large language models (LLMs) in multimodal scenarios. The framework iteratively improves the model’s ability to reason by reflecting on chain-of-thought (CoT) rationales, which are critical for solving complex tasks. This is achieved through two interconnected parts: bootstrapping positive and negative solutions for reasoning datasets and reflection on rationale for learning from mistakes. Specifically, self-refine and self-select losses are introduced to refine flawed rationale and derive the correct answer by comparing rationale candidates. Experimental results demonstrate that R3V consistently improves multimodal LLM reasoning, achieving a relative improvement of 23 to 60 percent over GPT-distilled baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart computer program that can understand and respond to language in a way that’s almost human-like. But sometimes, it gets stuck on tricky problems because it can’t think through things like humans do. Researchers came up with an idea called R3V to help these programs learn from their mistakes and become even better at solving puzzles. The method involves looking back at the thought process behind the program’s answers to see where it went wrong. By doing this, the program gets smarter and can solve problems more effectively. In tests, R3V helped the computer program improve its problem-solving skills by 23-60% compared to other methods. |
Keywords
» Artificial intelligence » Bootstrapping » Gpt » Self training
