DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911084124831744 |
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| author | Zhang, Yudong Xie, Ruobing Sun, Xingwu Huang, Yiqing Chen, Jiansheng Kang, Zhanhui Wang, Di Wang, Yu |
| author_facet | Zhang, Yudong Xie, Ruobing Sun, Xingwu Huang, Yiqing Chen, Jiansheng Kang, Zhanhui Wang, Di Wang, Yu |
| contents | Large vision-language models (LVLMs) have demonstrated exceptional performance on complex multimodal tasks. However, they continue to suffer from significant hallucination issues, including object, attribute, and relational hallucinations. To accurately detect these hallucinations, we investigated the variations in cross-modal attention patterns between hallucination and non-hallucination states. Leveraging these distinctions, we developed a lightweight detector capable of identifying hallucinations. Our proposed method, Detecting Hallucinations by Cross-modal Attention Patterns (DHCP), is straightforward and does not require additional LVLM training or extra LVLM inference steps. Experimental results show that DHCP achieves remarkable performance in hallucination detection. By offering novel insights into the identification and analysis of hallucinations in LVLMs, DHCP contributes to advancing the reliability and trustworthiness of these models. The code is available at https://github.com/btzyd/DHCP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18659 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models Zhang, Yudong Xie, Ruobing Sun, Xingwu Huang, Yiqing Chen, Jiansheng Kang, Zhanhui Wang, Di Wang, Yu Computer Vision and Pattern Recognition Artificial Intelligence Large vision-language models (LVLMs) have demonstrated exceptional performance on complex multimodal tasks. However, they continue to suffer from significant hallucination issues, including object, attribute, and relational hallucinations. To accurately detect these hallucinations, we investigated the variations in cross-modal attention patterns between hallucination and non-hallucination states. Leveraging these distinctions, we developed a lightweight detector capable of identifying hallucinations. Our proposed method, Detecting Hallucinations by Cross-modal Attention Patterns (DHCP), is straightforward and does not require additional LVLM training or extra LVLM inference steps. Experimental results show that DHCP achieves remarkable performance in hallucination detection. By offering novel insights into the identification and analysis of hallucinations in LVLMs, DHCP contributes to advancing the reliability and trustworthiness of these models. The code is available at https://github.com/btzyd/DHCP. |
| title | DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.18659 |