<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ETR</journal-id><journal-title-group><journal-title>Educational Theory and Research</journal-title></journal-title-group><issn>2995-3448</issn><eissn>2995-3456</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ETR.7146</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于ChatGLM 微调的医疗问答系统</title><url>https://artdesignp.com/journal/ETR/2/7/10.61369/ETR.7146</url><author>姚杰恺</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>2</volume><issue>7</issue><history><date date-type="pub"><published-time>2024-07-20</published-time></date></history><abstract>为提升大模型在医疗领域的专业性，本文采用Lora微调技术，利用huatuo26M数据集，对ChatGLM3-6B大模型进行微调，构建医疗问答系统。研究结果显示，该方法显著提高了医疗问答的专业度、准确性及对话流畅性。该系统在医疗咨询与健康指导中展现应用价值，并具备推广至其他专业领域大模型微调的潜力。</abstract><keywords>医疗问答系统，ChatGLM 模型，大模型，Lora 微调</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Du Z, Qian Y, Liu X,et al.GLM:General Language Model Pretraining with Autoregressive Blank Infilling［J］．2021.DOI:10.18653/v1/2022.acl-long.26.[2] 张伟，李明．基于深度学习的医疗问答系统研究综述．计算机工程与应用，2022. 58(12), 1-10.[3] 王晓霞，刘强．面向医疗领域的自然语言处理技术研究进展．智能科学与技术学报，(2021). 3(2), 152-160.[4] 赵瑞，陈志军．大型语言模型在医疗领域的应用与挑战．计算机科学，2020.47(6), 1-8.[5] 李华，张敏．自然语言处理技术在医疗问答系统中的应用．计算机应用研究，2019.36(10), 2925-2930.[6] Hu E J , Shen Y , Wallis P ,et al.LoRA: Low-Rank Adaptation of Large Language Models［J］．2021.DOI:10.48550/arXiv.2106.09685.[7] Li Jianquan, Wang Xidong, Wu Xiangbo, et al Huatuo-26M: A large-scale Chinese medical question and answer dataset［J］,2023.arXiv.https://arxiv.org/abs/2305.01526.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
