利用结构化医学知识库对大语言模型进行知识调优以实现中文可靠响应生成
Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese
AI总结：
针对大语言模型在医疗场景易生成事实幻觉的问题，提出基于结构化医学知识库的知识调优方法，发布中文医学知识问答数据集cMedKnowQA，实验证明该方法比普通指令调优准确率更高，为大模型领域适配提供新途径。
摘要：
大语言模型（LLMs）在通用领域的各类自然语言处理（NLP）任务中已取得显著成效。然而，由于领域知识有限，LLMs有时会生成包含医学事实幻觉的响应，这类缺陷给LLMs在医疗场景中的应用带来了潜在风险。为解决这一问题，我们提出知识调优（knowledge-tuning）方法，利用结构化医学知识库帮助LLMs高效掌握领域知识，助力生成可靠响应。我们还发布了cMedKnowQA，这是一个基于医学知识库构建的中文医学知识问答数据集，用于评估LLMs的医学知识掌握水平。实验结果表明，经cMedKnowQA知识调优的LLMs，在响应生成准确率上比普通指令调优的模型更高，为LLMs的领域适配提供了一种新的可靠途径。
英文摘要：
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 hallucination 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 reliable 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 reliable way for the domain adaptation of LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
