Summary of Improve Vision Language Model Chain-of-thought Reasoning, by Ruohong Zhang et al.
Improve Vision Language Model Chain-of-thought Reasoning
by Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, Yiming Yang
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper tackles the crucial issue of improving interpretability and trustworthiness in vision language models (VLMs) by enhancing chain-of-thought (CoT) reasoning. Current training recipes lack robust CoT reasoning data, relying on datasets with minimal rationales. The authors propose a two-fold approach to address this limitation: distilling rationales from GPT-4o and fine-tuning VLMs, as well as applying reinforcement learning to calibrate reasoning quality. Specifically, they construct positive and negative pairs of model-generated reasoning chains and refine the model’s abilities using Direct Preference Optimization. The authors demonstrate significant improvements in CoT reasoning on benchmark datasets and better generalization to direct answer prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how a computer vision model is making its decisions. This paper helps make those decisions more transparent by improving the way these models think through problems. Right now, training recipes for these models don’t provide enough information about why they’re making certain choices. The authors come up with two ways to fix this: first, they add more details to the training data, and second, they use a special kind of machine learning to make the model’s thinking more accurate. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Gpt » Machine learning » Optimization » Reinforcement learning
