RAR$^2$：通过思维驱动检索实现检索增强的医学推理
RAR$^2$: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval
AI总结：
本文提出RAR$^2$联合学习框架，以思维过程揭示隐含知识需求并指导检索与生成，结合DPO和测试时扩展策略提升复杂医学问答性能。
摘要：
大语言模型（LLM）已在多种医学基准上展现出良好性能，凸显了其支持真实临床任务的潜力。检索增强生成（RAG）已成为通过引入外部医学信息来缓解知识缺口和幻觉的关键方法。然而，RAG在处理需要密集推理的复杂医学问题时仍面临困难，因为表层输入往往无法反映任务真正的知识需求。现有方法通常聚焦于优化查询，却没有显式建模推理过程，从而限制了其检索并整合临床相关知识的能力。在本工作中，我们提出RAR$^2$，这是一个同时改进推理增强检索与检索增强推理的联合学习框架。RAR$^2$构建思维过程以揭示隐含的知识需求，并用其指导检索和答案生成。我们构建了一个混合偏好对训练数据集，并应用直接偏好优化（DPO）训练模型。此外，我们设计了两种测试时扩展策略，以探索该框架的边界。实验证明了RAR$^2$在多个生物医学问答数据集上的有效性，其表现优于经过或未经过微调的RAG基线。
英文摘要：
Large Language Models (LLMs) have shown promising performance on diverse medical benchmarks, highlighting their potential in supporting real-world clinical tasks. Retrieval-Augmented Generation (RAG) has emerged as a key approach for mitigating knowledge gaps and hallucinations by incorporating external medical information. However, RAG still struggles with complex medical questions that require intensive reasoning, as surface-level input often fails to reflect the true knowledge needs of the task. Existing methods typically focus on refining queries without explicitly modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge. In this work, we propose RAR$^2$, a joint learning framework that improves both Reasoning-Augmented Retrieval and Retrieval-Augmented Reasoning. RAR$^2$ constructs a thought process to uncover implicit knowledge requirements and uses it to guide retrieval and answer generation. We build a training dataset of mixed preference pairs and apply Direct Preference Optimization (DPO) to train the model. Moreover, we design two test-time scaling strategies to explore the boundaries of our framework. Experiments demonstrate the effectiveness of RAR$^2$ across several biomedical question answering datasets, outperforming RAG baselines with or without fine-tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
