@inproceedings{ding-etal-2025-eagle,
    title = "{EAGLE}: Expert-Guided Self-Enhancement for Preference Alignment in Pathology Large Vision-Language Model",
    author = "Ding, Meidan  and
      Zhang, Jipeng  and
      Wang, Wenxuan  and
      Zhong, Haiqin  and
      Wang, Xiaoqin  and
      Lyu, Xinheng  and
      Chen, Wenting  and
      Shen, Linlin",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.acl-long.711/",
    pages = "14603--14619",
    ISBN = "979-8-89176-251-0",
    abstract = "Recent advancements 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. While preference alignment methods have proven effective in general domains, acquiring high-quality preference data for pathology remains challenging due to limited expert resources and domain complexity. In this paper, we propose EAGLE (Expert-guided self-enhancement for preference Alignment in patholoGy Large vision-languagE model), a novel framework that systematically integrates medical expertise into preference alignment. EAGLE consists of three key stages: initialization through supervised fine-tuning, self-preference creation leveraging expert prompting and medical entity recognition, and iterative preference following-tuning. The self-preference creation stage uniquely combines expert-verified chosen sampling with expert-guided rejected sampling to generate high-quality preference data, while the iterative tuning process continuously refines both data quality and model performance. Extensive experiments demonstrate that EAGLE significantly outperforms existing pathological LVLMs, effectively reducing hallucination and bias while maintaining pathological accuracy. The source code is available at https://github.com/meidandz/EAGLE."
}