m-KAILIN: Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training

• URL: http://arxiv.org/abs/2504.19565v1
• Date: Mon, 28 Apr 2025 08:18:24 GMT
• Title: m-KAILIN: Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training
• Authors: Meng Xiao, Xunxin Cai, Chengrui Wang, Yuanchun Zhou,
• Abstract summary: We propose a knowledge-driven, multi-agent framework for scientific corpus distillation tailored for biomedical training.<n>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 data.
• Score: 8.238980609871042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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