用微调+检索增强生成，提升医学生物问答的准确性和推理能力。
MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation
- 结合微调与检索增强生成，动态引入领域知识。
- 在多个生物医学问答数据集上显著提升准确率。
- 适合医疗科研、临床决策和医学教育场景使用。
大型语言模型在自然语言处理任务中表现出色，但在医学与生物学等专业领域应用时，仍需优化以确保事实准确性、可靠性与上下文深度。我们提出 MedBioLM，一个针对生物医学问答任务优化的模型，旨在提升短文本与长文本查询的性能。通过融合微调与检索增强生成（RAG），MedBioLM能动态引入领域知识，增强推理能力与事实一致性。我们在涵盖结构化多选题与复杂临床推理任务的多种生物医学问答数据集上进行微调，结果显示微调显著提升了基准数据集上的准确率，而 RAG 进一步增强了答案的事实一致性。这些成果表明，经过领域优化的大模型在推动生物医学研究、医学教育及临床决策支持方面具有巨大潜力。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated impressive capabilities across natural language processing tasks. However, their application to specialized domains such as medicine and biology requires further optimization to ensure factual accuracy, reliability, and contextual depth. We introduce MedBioLM, a domain-adapted biomedical question-answering model designed to enhance both short-form and long-form queries. By integrating fine-tuning and retrieval-augmented generation (RAG), MedBioLM dynamically incorporates domain-specific knowledge, improving reasoning abilities and factual accuracy. To evaluate its effectiveness, we fine-tuned the model on diverse biomedical QA datasets, covering structured multiple-choice assessments and complex clinical reasoning tasks. Fine-tuning significantly improves accuracy on benchmark datasets, while RAG enhances factual consistency. These results highlight the potential of domain-optimized LLMs in advancing biomedical research, medical education, and clinical decision support.
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