@inproceedings{zhu-etal-2026-mitigating,
    title = "Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation",
    author = "Zhu, Xingyu  and
      Fang, Junfeng  and
      Wang, Shuo  and
      Zhu, Beier  and
      Wang, Zhicai  and
      Yang, Yonghui  and
      He, Xiangnan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.89/",
    pages = "1995--2009",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine tuning on annotated data devoid of hallucinations offers the most direct solution, while its high computational cost motivates recent representation-based methods, which focus on mitigating hallucinatory components within hidden representations. Though efficient, we empirically observe that these methods degrade general generation capacity due to incomplete extraction of hallucination components and non-selective parameter updates. To address these limitations, we propose MPD, a dual-stage framework for mitigating hallucinations without performance degradation. Specifically, our MPD relies on two essential factors: (1) semantic-aware component disentanglement to extract pure hallucination components, and (2) interpretable parameter updates that selectively modify parameters most relevant to hallucination. Extensive experiments demonstrate that MPD achieves state-of-the-art performance, reducing hallucinations by 23.4{\%} while maintaining 97.4{\%} of general generative capability as evaluated on LLaVA-Bench and MME, with no additional computational cost."
}