Large Language Models (LLMs) have demonstrated significant enhancements in
instruction-following abilities through instruction tuning, achieving notable
performances across various tasks. Previous research has focused on fine-tuning
medical domain-specific LLMs using an extensive array of medical-specific data,
incorporating millions of pieces of biomedical literature to augment their
medical capabilities. However, existing medical instruction-tuned LLMs have
been constrained by the limited scope of tasks and instructions available,
restricting the efficacy of instruction tuning and adversely affecting
performance in the general domain. In this paper, we fine-tune LLaMA-series
models using 52k diverse, machine-generated, medical instruction-following
data, MedInstruct-52k, resulting in the model AlpaCare. Comprehensive
experimental results on both general and medical-specific domain free-form
instruction evaluations showcase AlpaCare's strong medical proficiency and
generalizability compared to previous instruction-tuned models in both medical
and general domains. We provide public access to our MedInstruct-52k dataset
and a clinician-crafted free-form instruction test set, MedInstruct-test, along
with our codebase, to foster further research and development. Our project page
is available at this https URL

通过 fine-tune LLaMA 系列模型使用 52k 多样化的、机器生成的医疗指令数据 MedInstruct-52k，得到模型 AlpaCare，研究对比了 AlpaCare 与以往指令调整模型在医学和一般领域中的医疗能力和通用性，结果显示 AlpaCare 在医学和一般领域中具有较强的医学能力和通用性。