MedBioLM：利用微调大语言模型与检索增强生成优化医学和生物问答
MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation
AI总结：
MedBioLM通过微调与检索增强生成优化生物医学问答，在多项选择和临床推理任务上提升准确率与事实一致性，展示了大语言模型在医学研究和临床决策支持中的潜力。
摘要：
大语言模型（LLMs）已在自然语言处理任务中展现出卓越能力。然而，将其应用于医学和生物学等专业领域仍需进一步优化，以确保事实准确性、可靠性和上下文深度。我们提出了MedBioLM，这是一种面向生物医学领域的问答模型，旨在增强短式和长式查询的处理能力。通过整合微调与检索增强生成（RAG），MedBioLM能够动态引入领域特定知识，从而提升推理能力和事实准确性。为评估其有效性，我们在多样化的生物医学问答数据集上对模型进行了微调，这些数据集涵盖结构化多项选择评估和复杂临床推理任务。微调显著提高了基准数据集上的准确率，而RAG则增强了事实一致性。这些结果凸显了领域优化的大语言模型在推动生物医学研究、医学教育和临床决策支持方面的潜力。
英文摘要：
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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