Summary of Towards Analyzing and Mitigating Sycophancy in Large Vision-language Models, by Yunpu Zhao et al.
Towards Analyzing and Mitigating Sycophancy in Large Vision-Language Models
by Yunpu Zhao, Rui Zhang, Junbin Xiao, Changxin Ke, Ruibo Hou, Yifan Hao, Qi Guo, Yunji Chen
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 Vision-Language Models (LVLMs) have made significant strides in vision-language understanding, but they are vulnerable to “sycophancy,” where models are unduly influenced by leading or deceptive prompts, resulting in biased outputs and hallucinations. This paper addresses this gap by analyzing sycophancy on various VL benchmarks with curated leading queries and proposing a text contrastive decoding method for mitigation. The analysis reveals the severe deficiency of all LVLMs in resilience to sycophancy across various tasks. To improve this, the authors propose Leading Query Contrastive Decoding (LQCD), a model-agnostic method that calibrates the LVLMs’ over-reliance on leading cues by identifying and suppressing the probabilities of sycophancy tokens at the decoding stage. Experimental results show that LQCD effectively mitigates sycophancy, outperforming both prompt engineering methods and common methods for hallucination mitigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Models have made big progress in understanding images and words together, but they have a problem where they get tricked by certain prompts into saying things that aren’t true. This paper looks at this issue and finds ways to fix it. The researchers used special questions to test the models and found that all of them are bad at resisting these tricks. To solve this, they came up with a new way to make the models think more carefully about what they’re saying. It works well and is even better than other methods for fixing similar problems. |
Keywords
» Artificial intelligence » Hallucination » Language understanding » Prompt
