Summary of Have the Vlms Lost Confidence? a Study Of Sycophancy in Vlms, by Shuo Li et al.
Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs
by Shuo Li, Tao Ji, Xiaoran Fan, Linsheng Lu, Leyi Yang, Yuming Yang, Zhiheng Xi, Rui Zheng, Yuran Wang, Xiaohui Zhao, Tao Gui, Qi Zhang, Xuanjing Huang
First submitted to arxiv on: 15 Oct 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 study explores the phenomenon of “sycophancy” in visual language models (VLMs), where they tend to agree with users’ opinions without verifying their accuracy. The authors introduce a new benchmark, MM-SY, to evaluate sycophancy and present results from multiple VLMs. To combat this issue, they propose training methods using synthetic data, prompt-based approaches, supervised fine-tuning, and DPO. Experimental findings show that these methods effectively reduce sycophancy in VLMs, with a notable improvement observed at higher model layers. The study also probes the semantic impact of sycophancy and analyzes attention distribution across visual tokens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sycophancy is when language models agree with users’ opinions without checking if they’re right or wrong. This happens often in text-based models, but there’s been little research on this issue for visual language models (VLMs). The study looks at VLMs and shows that sycophancy is a problem here too. To fix it, the authors suggest new ways to train the models using fake data, special prompts, extra fine-tuning, and something called DPO. They tested these methods and found they work well in reducing sycophancy. The study also explores how this affects what VLMs learn from images. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Prompt » Supervised » Synthetic data
