Medical large language models (LLMs) have gained popularity recently due to their significant practical utility. However, most existing research focuses on general medicine, and there is a need for in-depth study of LLMs in specific fields like anesthesiology. To fill the gap, we introduce Hypnos, a Chinese Anesthesia model built upon existing LLMs, e.g., Llama. Hypnos' contributions have three aspects: 1) The data, such as utilizing Self-Instruct, acquired from current LLMs likely includes inaccuracies. Hypnos implements a cross-filtering strategy to improve the data quality. This strategy involves using one LLM to assess the quality of the generated data from another LLM and filtering out the data with low quality. 2) Hypnos employs a general-to-specific training strategy that starts by fine-tuning LLMs using the general medicine data and subsequently improving the fine-tuned LLMs using data specifically from Anesthesiology. The general medical data supplement the medical expertise in Anesthesiology and enhance the effectiveness of Hypnos' generation. 3) We introduce a standardized benchmark for evaluating medical LLM in Anesthesiology. Our benchmark includes both publicly available instances from the Internet and privately obtained cases from the Hospital. Hypnos outperforms other medical LLMs in anesthesiology in metrics, GPT-4, and human evaluation on the benchmark dataset.

翻译：近年来，医学大语言模型因其显著的实际应用价值而备受关注。然而，现有研究多集中于通用医学领域，针对麻醉学等特定领域的语言模型仍需深入探索。为弥补这一空白，我们提出Hypnos——一个基于现有大语言模型（如Llama）构建的中文麻醉学模型。Hypnos的贡献体现在三个方面：1）针对当前利用Self-Instruct等方法从语言模型获取的数据可能存在的误差问题，Hypnos采用跨模型过滤策略提升数据质量。该策略通过利用一个语言模型评估另一模型生成的数据质量，并过滤低质量数据。2）Hypnos采用“通用到专用”的训练策略：先使用通用医学数据微调语言模型，再基于麻醉学专项数据优化模型。通用医学数据补充了麻醉学所需的医学知识，提升了Hypnos的生成效果。3）我们构建了一个标准化基准，用于评估麻醉学领域的医学语言模型。该基准包含互联网公开实例和医院非公开病例两种来源。在基准数据集上，Hypnos在客观指标、GPT-4对比及人工评估中均优于其他麻醉学医学语言模型。