Recent advances in large language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, their effective application in the medical domain is hampered by a lack of medical domain knowledge. In this study, we present SA-MDKIF, a scalable and adaptable framework that aims to inject medical knowledge into general-purpose LLMs through instruction tuning, thereby enabling adaptability for various downstream tasks. SA-MDKIF consists of two stages: skill training and skill adaptation. In the first stage, we define 12 basic medical skills and use AdaLoRA to train these skills based on uniformly formatted instructional datasets that we have constructed. In the next stage, we train the skill router using task-specific downstream data and use this router to integrate the acquired skills with LLMs during inference. Experimental results on 9 different medical tasks show that SA-MDKIF improves performance by 10-20% compared to the original LLMs. Notably, this improvement is particularly pronounced for unseen medical tasks, showing an improvement of up to 30%.

我们提出了SA-MDKIF，它是一个可扩展和适应性强的框架，通过指令调整将医学知识注入通用语言模型，从而使其能够适应各种下游任务，实验证明SA-MDKIF相比原始的大型语言模型在医学任务中的性能提升了10-20%，对于未见过的医学任务，性能提升高达30%。

SA-MDKIF：大规模语言模型的可扩展且适应性强的医学领域知识注入框架