@inproceedings{ma-etal-2025-prefix,
    title = "Prefix-Enhanced Large Language Models with Reused Training Data in Multi-Turn Medical Dialogue",
    author = "Ma, Suxue  and
      Yang, Zhicheng  and
      Lin, Ruei-Sung  and
      Tang, Youbao  and
      Zhang, Ning  and
      Cao, Zhenjie  and
      Ni, Yuan  and
      Xiao, Jing  and
      Hou, Jieke  and
      Chang, Peng",
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.cl4health-1.3/",
    pages = "26--33",
    ISBN = "979-8-89176-238-1",
    abstract = "Large Language Models have made impressive progress in the medical field. In medical dialogue scenarios, unlike traditional single-turn question-answering tasks, multi-turn doctor-patient dialogue tasks require AI doctors to interact with patients in multiple rounds, where the quality of each response impacts the overall model performance. In this paper, we propose PERT to re-explore values of multi-turn dialogue training data after the supervised fine-tuning phase by integrating a prefix learning strategy, further enhancing the response quality. Our preliminary results show that PERT achieves notable improvements on gynecological data, with an increase of up to 0.22 on a 5-point rating scale."
}