@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://aclanthology.org/2025.cl4health-1.3/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T Prefix-Enhanced Large Language Models with Reused Training Data in Multi-Turn Medical Dialogue
%A Ma, Suxue
%A Yang, Zhicheng
%A Lin, Ruei-Sung
%A Tang, Youbao
%A Zhang, Ning
%A Cao, Zhenjie
%A Ni, Yuan
%A Xiao, Jing
%A Hou, Jieke
%A Chang, Peng
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%Y Gupta, Deepak
%Y Thompson, Paul
%S Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-238-1
%F ma-etal-2025-prefix
%X 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.
%R 10.18653/v1/2025.cl4health-1.3
%U https://aclanthology.org/2025.cl4health-1.3/
%U https://doi.org/10.18653/v1/2025.cl4health-1.3
%P 26-33
Markdown (Informal)
[Prefix-Enhanced Large Language Models with Reused Training Data in Multi-Turn Medical Dialogue](https://aclanthology.org/2025.cl4health-1.3/) (Ma et al., CL4Health 2025)
ACL
- Suxue Ma, Zhicheng Yang, Ruei-Sung Lin, Youbao Tang, Ning Zhang, Zhenjie Cao, Yuan Ni, Jing Xiao, Jieke Hou, and Peng Chang. 2025. Prefix-Enhanced Large Language Models with Reused Training Data in Multi-Turn Medical Dialogue. In Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health), pages 26–33, Albuquerque, New Mexico. Association for Computational Linguistics.