Summary of Dhcp: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-language Models, By Yudong Zhang et al.
DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models
by Yudong Zhang, Ruobing Xie, Jiansheng Chen, Xingwu Sun, Zhanhui kang, Yu Wang
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed paper investigates large vision-language models’ (LVLMs) exceptional performance on complex multimodal tasks while addressing their significant hallucination issues. Researchers analyzed variations in cross-modal attention patterns between hallucination and non-hallucination states, developing a lightweight detector to accurately identify these hallucinations. The proposed method, Detecting Hallucinations by Cross-modal Attention Patterns (DHCP), is straightforward and doesn’t require additional LVLM training or extra inference steps. DHCP achieves remarkable performance in hallucination detection, contributing to advancing the reliability and trustworthiness of LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision-language models can do many things, but they sometimes make mistakes. These mistakes are called “hallucinations.” Hallucinations are when the model thinks it sees or hears something that isn’t really there. To fix this problem, researchers looked at how the model pays attention to different parts of a picture and what it’s listening to. They found patterns in the way the model pays attention when it makes mistakes. Using these patterns, they created a simple tool to detect when the model is hallucinating. This tool works well and helps make sure we can trust the models. |
Keywords
» Artificial intelligence » Attention » Hallucination » Inference
