The rapid progress of large language models (LLMs) in biomedical research has underscored the limitations of existing open-source annotated scientific corpora, which are often insufficient in quantity and quality. Addressing the challenge posed by the complex hierarchy of biomedical knowledge, we propose a knowledge-driven, multi-agent framework for scientific corpus distillation tailored for LLM training in the biomedical domain. Central to our approach is a collaborative multi-agent architecture, where specialized agents, each guided by the Medical Subject Headings (MeSH) hierarchy, work in concert to autonomously extract, synthesize, and self-evaluate high-quality textual data from vast scientific literature. These agents collectively generate and refine domain-specific question-answer pairs, ensuring comprehensive coverage and consistency with biomedical ontologies while minimizing manual involvement. Extensive experimental results show that language models trained on our multi-agent distilled datasets achieve notable improvements in biomedical question-answering tasks, outperforming both strong life sciences LLM baselines and advanced proprietary models. Notably, our AI-Ready dataset enables Llama3-70B to surpass GPT-4 with MedPrompt and Med-PaLM-2, despite their larger scale. Detailed ablation studies and case analyses further validate the effectiveness and synergy of each agent within the framework, highlighting the potential of multi-agent collaboration in biomedical LLM training.

本研究针对现有开放式注释科学语料的数量和质量不足的问题，提出了一种知识驱动的多代理框架，以支持生物医学领域的大语言模型训练。通过合作多代理架构，专门的代理基于医学主题词层级提取和合成高质量文本数据，显著提升了生物医学问答任务的表现，生成的AI准备数据集在一定程度上超越了现有先进模型。

m-KAILIN：面向生物医学大语言模型训练的知识驱动代理科学语料提取框架