This study presents a comprehensive analysis and comparison of two
predominant fine-tuning methodologies - full-parameter fine-tuning and
parameter-efficient tuning - within the context of medical Large Language
Models (LLMs). We developed and refined a series of LLMs, based on the Llama-2
architecture, specifically designed to enhance medical knowledge retrieval,
reasoning, and question-answering capabilities. Our experiments systematically
evaluate the effectiveness of these tuning strategies across various well-known
medical benchmarks. Notably, our medical LLM Med42 showed an accuracy level of
72% on the US Medical Licensing Examination (USMLE) datasets, setting a new
standard in performance for openly available medical LLMs. Through this
comparative analysis, we aim to identify the most effective and efficient
method for fine-tuning LLMs in the medical domain, thereby contributing
significantly to the advancement of AI-driven healthcare applications.

该研究对两种主要的微调方法 —— 全参数微调和参数高效微调 —— 在医学大型语言模型（LLM）领域进行了全面的分析和比较。通过开发和优化一系列基于 Llama-2 架构的医学 LLM，从而提升医学知识检索、推理和问答的能力，我们系统地评估了这些微调策略在各种知名医学基准测试上的有效性。值得注意的是，我们的医学 LLM Med42 在美国医学执照考试（USMLE）数据集上表现出了 72% 的准确率，为公开可用的医学 LLM 性能树立了新的标准。通过这种比较分析，我们旨在找到医学领域 LLM 微调的最有效和高效方法，从而在推动以人工智能驱动的医疗应用方面做出重大贡献。