ChatDoctor：使用医学领域知识在大型语言模型Meta-AI (LLaMA)上微调的医学聊天模型
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge
AI总结：
本研究通过在10万条医患对话数据上微调LLaMA并引入自主信息检索机制，提出了医学聊天模型ChatDoctor，显著提升了模型理解患者需求和提供准确医疗建议的能力。
摘要：
本研究的主要目的是通过创建一个在医疗建议上具有更高准确性的专用语言模型，来解决当前流行的大型语言模型（LLMs）（如ChatGPT）在医学知识方面存在的局限性。我们通过使用来自一个广泛使用的在线医疗咨询平台的100,000条患者-医生对话的大型数据集，对大型语言模型meta-AI (LLaMA) 进行了适配和精炼来实现这一目标。这些对话经过清洗和匿名化处理，以尊重隐私问题。除了模型精炼外，我们还引入了一个自主信息检索机制，允许模型访问和利用来自Wikipedia等在线来源的实时信息以及来自精选的离线医学数据库的数据。使用真实世界的患者-医生互动对模型进行微调，显著提高了模型理解患者需求并提供知情建议的能力。通过为模型配备从可靠的在线和离线来源进行自主信息检索的能力，我们观察到其响应的准确性有了实质性提高。我们提出的ChatDoctor代表了医学LLMs的一项重大进步，在理解患者询问和提供准确建议方面展现出显著提升。鉴于医学领域的高风险和低容错率，这种在提供准确和可靠信息方面的增强不仅是有益的，而且是必不可少的。
英文摘要：
The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specialized language model with enhanced accuracy in medical advice. We achieved this by adapting and refining the large language model meta-AI (LLaMA) using a large dataset of 100,000 patient-doctor dialogues sourced from a widely used online medical consultation platform. These conversations were cleaned and anonymized to respect privacy concerns. In addition to the model refinement, we incorporated a self-directed information retrieval mechanism, allowing the model to access and utilize real-time information from online sources like Wikipedia and data from curated offline medical databases. The fine-tuning of the model with real-world patient-doctor interactions significantly improved the model's ability to understand patient needs and provide informed advice. By equipping the model with self-directed information retrieval from reliable online and offline sources, we observed substantial improvements in the accuracy of its responses. Our proposed ChatDoctor, represents a significant advancement in medical LLMs, demonstrating a significant improvement in understanding patient inquiries and providing accurate advice. Given the high stakes and low error tolerance in the medical field, such enhancements in providing accurate and reliable information are not only beneficial but essential.
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。
