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arXiv 2304.06975cs.CL
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华佗:利用中文医学知识微调LLaMA模型

HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge

Harbin Institute of Technology(哈尔滨工业大学)

Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang, Sendong Zhao, Bing Qin, Ting Liu

AI总结:

针对LLMs在生物医学领域表现不佳的问题,本文提出基于LLaMA的华佗模型,利用生成的中文医学问答实例进行监督微调,实验证明其回答具有更可靠的医学知识。

摘要:

大型语言模型(LLMs),如LLaMA模型,已在各种通用领域的自然语言处理(NLP)任务中展现出有效性。然而,由于医学领域回答需要专业知识,LLMs在生物医学领域任务中尚未达到最佳表现。为应对这一挑战,我们提出了华佗(HuaTuo),这是一个基于LLaMA的模型,通过生成的问答(QA)实例进行监督微调。实验结果表明,华佗生成的回答具备更可靠的医学知识。我们提出的华佗模型可在 https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese 获取。

英文摘要:

Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks. Nevertheless, LLMs have not yet performed optimally in biomedical domain tasks due to the need for medical expertise in the responses. In response to this challenge, we propose HuaTuo, a LLaMA-based model that has been supervised-fine-tuned with generated QA (Question-Answer) instances. The experimental results demonstrate that HuaTuo generates responses that possess more reliable medical knowledge. Our proposed HuaTuo model is accessible at https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese.

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。

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