Summary of Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-language Models, by Weihong Zhong et al.
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
by Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper investigates the limitations of Large Vision-Language Models (LVLMs) in understanding visual information when interacting with human languages. The authors identify a phenomenon called multimodal hallucinations, where LVLMs generate false claims based on previously generated hallucinations. They propose a framework called MMHalSnowball to evaluate the impact of these hallucinations on LVLM performance and demonstrate that open-source LVLMs are prone to accept generated hallucinations, leading to a drop in performance by at least 31%. To mitigate this issue, the authors introduce Residual Visual Decoding, a training-free method that revises the output distribution of LVLMs based on residual visual input. Experiments show that this approach can reduce snowballed multimodal hallucination by more than 24% while maintaining capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Vision-Language Models (LVLMs) understand what they see and hear. They found that these models are easily tricked into saying false things because of something called “multimodal hallucinations”. This means that if a model is shown an image and then asked to describe it, it might say something that isn’t really there just because it was given the wrong information earlier. The researchers created a special test to see how this affects LVLMs’ performance and found that they get 31% worse at understanding what they see when they’re given misleading information. To fix this problem, they came up with a way to help LVLMs understand what’s real and what’s not, which worked really well. |
Keywords
» Artificial intelligence » Hallucination
