Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin
Abstract
Though advanced in understanding visual information with human languages, Large Vision-Language Models (LVLMs) still suffer from multimodal hallucinations. A natural concern is that during multimodal interaction, the generated hallucinations could influence the LVLMs’ subsequent generation. Thus, we raise a question: When presented with a query relevant to the previously generated hallucination, will LVLMs be misled and respond incorrectly, even though the ground visual information exists? To answer this, we propose a framework called \\textitMMHalSnowball to evaluate LVLMs’ behaviors when encountering generated hallucinations, where LVLMs are required to answer specific visual questions within a curated hallucinatory conversation. Crucially, our experiment shows that the performance of open-source LVLMs drops by at least 31\\%, indicating that LVLMs are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions. We term this Multimodal Hallucination Snowballing. To mitigate this issue, we further propose a training-free method called Residual Visual Decoding, where we revise the output distribution of LVLMs with the one derived from the residual visual input, providing models with direct access to the visual information. Experiments show that our method can mitigate more than 24\\% of the snowballed multimodal hallucination while maintaining capabilities.- Anthology ID:
- 2024.acl-long.648
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11991–12011
- Language:
- URL:
- https://aclanthology.org/2024.acl-long.648
- DOI:
- 10.18653/v1/2024.acl-long.648
- Cite (ACL):
- Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, and Bing Qin. 2024. Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11991–12011, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (Zhong et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.acl-long.648.pdf