Summary of Enhancing Visual-language Modality Alignment in Large Vision Language Models Via Self-improvement, by Xiyao Wang et al.
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
by Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Furong Huang, Cao Xiao
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 proposes a novel framework called SIMA for enhancing visual and language modality alignment in large vision-language models (LVLMs) without relying on external dependencies. The proposed approach leverages existing vision instruction tuning datasets to self-generate responses and incorporates an in-context self-critic mechanism that constructs preference pairs for tuning. The framework allows LVLMs to act as critics by designing effective critic prompts, eliminating the need for additional fine-tuning with external instruction data. The paper also introduces three novel visual metrics within the self-critic process to guide judgment, significantly improving the accuracy of self-criticism. Through extensive experiments across 14 hallucination and comprehensive benchmarks, the authors demonstrate that SIMA improves LVLM’s performance and outperforms previous approaches, achieving superior modality alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how big AI models can improve at combining pictures and words. Right now, these models are very good at answering questions about what they see, but there is still room for improvement in making sure the pictures and words match up well. Some methods that try to do this rely on other models or data, which isn’t always reliable. The authors of this paper propose a new way to improve alignment without relying on outside help. They use existing training data to generate responses and add a special mechanism that lets the model judge its own work. This approach is more accurate than previous methods and can even make the model better at understanding pictures. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Hallucination » Instruction tuning
