MedChatZH：更好的医学顾问源于更好的指令
MedChatZH: a Better Medical Adviser Learns from Better Instructions
AI总结：
针对传统中医问答中通用大模型表现不佳的问题，提出MedChatZH对话模型，通过医学书籍预训练和指令微调，在真实医学对话数据上超越多个基线，并开源资源。
摘要：
生成式大语言模型（LLMs）在包括问答（QA）和对话系统在内的各种应用中已展现出巨大成功。然而，在传统中医问答等专业领域，这些模型若未在领域特定数据集上进行微调，可能表现不佳。为解决此问题，我们引入了MedChatZH，一个专门为传统中医问答设计的对话模型。我们的模型在中文传统医学书籍上进行了预训练，并使用精心策划的医学指令数据集进行了微调。在真实世界的医学对话数据集上，它优于多个坚实的基线模型。我们发布了我们的模型、代码和数据集，网址为https://github.com/tyang816/MedChatZH，以促进传统中医和LLMs领域的进一步研究。
英文摘要：
Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains like traditional Chinese medical QA, these models may perform unsatisfactorily without fine-tuning on domain-specific datasets. To address this, we introduce MedChatZH, a dialogue model designed specifically for traditional Chinese medical QA. Our model is pre-trained on Chinese traditional medical books and fine-tuned with a carefully curated medical instruction dataset. It outperforms several solid baselines on a real-world medical dialogue dataset. We release our model, code, and dataset on https://github.com/tyang816/MedChatZH to facilitate further research in the domain of traditional Chinese medicine and 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。
