Instruction-finetuning (IFT) has become crucial in aligning Large Language Models (LLMs) with diverse human needs and has shown great potential in medical applications. However, previous studies mainly fine-tune LLMs on biomedical datasets with limited diversity, which often rely on benchmarks or narrow task scopes, and hence significantly limit the effectiveness on their medical instruction-following ability and generalizability. To bridge this gap, we propose creating a diverse, machine-generated medical IFT dataset, MedInstruct-52k, using GPT-4 and ChatGPT with a high-quality expert-curated seed set. We then fine-tune LLaMA-series models on the dataset to develop AlpaCare. Despite using a smaller domain-specific dataset than previous medical LLMs, AlpaCare not only demonstrates superior performance on medical applications, with up to 38.1% absolute gain over best baselines in medical free-form instruction evaluations, but also achieves 6.7% absolute gains averaged over multiple general domain benchmarks. Human evaluation further shows that AlpaCare consistently outperforms best baselines in terms of both correctness and helpfulness. We offer public access to our data, model, and codebase in https://github.com/XZhang97666/AlpaCare.

翻译：指令微调（IFT）已成为使大语言模型（LLMs）与多样化人类需求对齐的关键技术，并在医学应用中展现出巨大潜力。然而，现有研究主要在多样性有限的生物医学数据集上微调LLMs，这些数据集通常依赖基准测试或狭窄任务范围，从而显著限制了模型在医学指令遵循能力和泛化性方面的有效性。为弥补这一不足，我们提出利用GPT-4和ChatGPT，基于高质量专家精选种子集，创建多样化、机器生成的医学IFT数据集MedInstruct-52k。随后，我们在该数据集上微调LLaMA系列模型，开发出AlpaCare。尽管使用的领域专用数据集规模小于先前医学LLMs，AlpaCare不仅在医学应用中展现出卓越性能——在医学自由形式指令评估中相较最佳基线取得高达38.1%的绝对增益，而且在多个通用领域基准测试中平均获得6.7%的绝对增益。人工评估进一步表明，AlpaCare在正确性和有用性方面均持续优于最佳基线。我们已在https://github.com/XZhang97666/AlpaCare 开放数据、模型及代码库。