  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.
