Summary of Catch: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in Lvlms, by Zhehan Kan et al.
CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs
by Zhehan Kan, Ce Zhang, Zihan Liao, Yapeng Tian, Wenming Yang, Junyuan Xiao, Xu Li, Dongmei Jiang, Yaowei Wang, Qingmin Liao
First submitted to arxiv on: 19 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 paper introduces Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in Large Vision-Language Model (LVLM) systems. These models have shown impressive reasoning capabilities but suffer from severe hallucination issues, posing risks in domains like healthcare and autonomous systems. The authors develop CATCH based on Information Bottleneck theory to address visual defects caused by vision-language misalignment. CATCH includes Complementary Visual Decoupling for information separation, Non-Visual Screening for hallucination detection, and Adaptive Token-level Contrastive Decoding for mitigation. This approach addresses issues related to diminished fine-grained feature perception and cumulative hallucinations in open-ended scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Model (LVLM) systems are super smart, but they can also make mistakes! These models are great at understanding pictures and words together, but sometimes they get confused and see things that aren’t really there. This is a big problem because it can lead to bad decisions in important areas like hospitals or self-driving cars. The researchers came up with a new way to fix this called CATCH. It’s like having a special filter that helps the model be more accurate and not make false claims. CATCH is really good at solving problems and works well even when it doesn’t have any specific training data! |
Keywords
» Artificial intelligence » Hallucination » Language model » Token
