AlpaCare：用于医疗应用的指令微调大型语言模型
AlpaCare:Instruction-tuned Large Language Models for Medical Application
AI总结：
本文提出使用GPT-4和ChatGPT生成多样化医疗指令微调数据集MedInstruct-52k，并基于LLaMA微调出AlpaCare模型，在医疗和通用任务上均超越基线，并公开了数据与代码。
摘要：
指令微调（IFT）在使大型语言模型（LLMs）与多样化的人类需求对齐方面变得至关重要，并已在医疗应用中展现出巨大潜力。然而，以往的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于基准测试或狭窄的任务范围，因此显著限制了其在医疗指令遵循能力和泛化性方面的有效性。为了弥补这一差距，我们提出使用GPT-4和ChatGPT结合高质量的专家精选种子集，创建一个多样化的、机器生成的医疗IFT数据集MedInstruct-52k。然后，我们在此数据集上微调LLaMA系列模型以开发AlpaCare。尽管使用的特定领域数据集比以往的医疗LLMs更小，AlpaCare不仅在医疗应用中表现出卓越的性能（在医疗自由形式指令评估中，比最佳基线高出绝对38.1%的增益），而且在多个通用领域基准测试中平均也获得了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始终优于最佳基线。我们在https://github.com/XZhang97666/AlpaCare公开提供我们的数据、模型和代码库。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
