Papers with multimodal hallucination
Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision (2024.naacl-long)
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| Challenge: | Recent studies have conjectured that multimodal hallucination is due to the vision encoder failing to ground on the image properly. |
| Approach: | They propose a multimodal self-feedback guided revision model that leverages visual cues to generate feedback to its initial response based on the visual information provided by the vision encoder. |
| Outcome: | The proposed model reduces multimodal hallucination and outperforms previous models on MMHal-Bench, POPE, and GAVIE. |
HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (2024.emnlp-main)
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| Challenge: | Existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics. |
| Approach: | They propose a framework that incorporates hallucination feedback at both object and sentence semantic levels to alleviate over 15% of hallucinism. |
| Outcome: | The proposed framework can alleviate over 15% of hallucination even with a marginal degree of training. |
EAGLE: Expert-Guided Self-Enhancement for Preference Alignment in Pathology Large Vision-Language Model (2025.acl-long)
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Meidan Ding, Jipeng Zhang, Wenxuan Wang, Haiqin Zhong, Xiaoqin Wang, Xinheng Lyu, Wenting Chen, Linlin Shen
| Challenge: | Recent advances in Large Vision Language Models (LVLMs) show promise for pathological diagnosis, yet their application in clinical settings faces critical challenges of multimodal hallucination and biased responses. |
| Approach: | They propose a framework that integrates medical expertise into preference alignment. |
| Outcome: | The proposed framework outperforms existing pathological LVLMs while maintaining pathological accuracy. |
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)
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Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang
| Challenge: | Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment. |
| Approach: | They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data . |
| Outcome: | The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech. |