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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>\n            Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains. However, LLMs sometimes generate responses with the\n            <jats:italic>hallucination<\/jats:italic>\n            about medical facts due to limited domain knowledge. Such shortcomings pose potential risks in the utilization of LLMs within medical contexts. To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate trustworthy response generation. We also release cMedKnowQA, a Chinese medical knowledge question-answering dataset constructed from medical knowledge bases to assess the medical knowledge proficiency of LLMs. Experimental results show that the LLMs which are knowledge-tuned with cMedKnowQA can exhibit higher levels of accuracy in response generation compared with vanilla instruction-tuning and offer a new trustworthy way for the domain adaptation of LLMs. We release our code and data at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/SCIR-HI\/Huatuo-Llama-Med-Chinese\">https:\/\/github.com\/SCIR-HI\/Huatuo-Llama-Med-Chinese<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3686807","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T18:28:41Z","timestamp":1722968921000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Trustworthy Response Generation in Chinese"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2908-9750","authenticated-orcid":false,"given":"Haochun","family":"Wang","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4676-1812","authenticated-orcid":false,"given":"Sendong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9912-0967","authenticated-orcid":false,"given":"Zewen","family":"Qiang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5735-3598","authenticated-orcid":false,"given":"Zijian","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7028-4124","authenticated-orcid":false,"given":"Chi","family":"Liu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4813-0821","authenticated-orcid":false,"given":"Nuwa","family":"Xi","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6821-7690","authenticated-orcid":false,"given":"Yanrui","family":"Du","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2543-5604","authenticated-orcid":false,"given":"Bing","family":"Qin","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9091-7757","authenticated-orcid":false,"given":"Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]}],"member":"320","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Yuntao Bai Andy Jones Kamal Ndousse Amanda Askell Anna Chen Nova DasSarma Dawn Drain Stanislav Fort Deep Ganguli Tom Henighan Nicholas Joseph Saurav Kadavath Jackson Kernion Tom Conerly Sheer El-Showk Nelson Elhage Zac Hatfield-Dodds Danny Hernandez Tristan Hume Scott Johnston Shauna Kravec Liane Lovitt Neel Nanda Catherine Olsson Dario Amodei Tom Brown Jack Clark Sam McCandlish Chris Olah Ben Mann and Jared Kaplan. 2022. 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