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%。