Information-Theoretic Domain Adaptative Training of Medicine Language Models with Rigorous Generalization

Contemporary advancements in large language models (LLMs), both proprietary and open-source, have demonstrated capabilities surpassing human performance across diverse general domains. Nevertheless, their efficacy in specialized professional fields, particularly medicine, remains suboptimal within the open-source ecosystem. To address these limitations, we introduce Domain Adaptative Medicine-LLM (DAMe-LLM). Our approach encompasses continued pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF) methodologies. In the development of DAMe-LLM, we have curated an extensive Chinese and English medical dataset for continued pre-training, complemented by a high-fidelity SFT dataset encompassing a broad spectrum of medical specialties. Our contribution provides the scientific community with valuable resources for further investigation and development in the domain of medicine artificial intelligence.