AI 中文总结
研究生物医学问答中不同问题类型的有效检索策略，提出自适应检索框架，依问题类型选检索和证据聚合策略，结合多种技术，经实验验证该策略能提升证据相关性和答案质量，为增强相关问答系统提供有效方向。
AI 中文摘要
生物医学问答包含多种问题类型，如是非题、事实类问题、列表类问题和总结类问题，每种都需要不同形式的证据和推理。但多数检索增强型问答系统采用统一检索流程，未考虑不同问题类别的信息需求，可能限制证据获取及下游答案生成效果。本文提出一种自适应检索框架，根据问题类型选择检索和证据聚合策略。该系统结合查询理解、生物医学文档检索、重排、知识图谱增强、文档聚类及基于大语言模型的答案生成。对于是非题，注重精确证据检索；事实类和列表类问题，强调面向实体的检索和聚类；总结类问题，则进行更广泛的证据收集与综合。在BioASQ基准上评估该框架，结果表明自适应检索策略能提高多种问题类型的证据相关性和答案质量，为增强检索增强型生物医学问答系统提供了有效方向。
英文摘要
Biomedical question answering (QA) encompasses diverse question types, including yes/no, factoid, list, and summary questions, each requiring distinct forms of evidence and reasoning. However, most retrieval-augmented QA systems rely on a unified retrieval pipeline, regardless of the information needs of different question categories. This one-size-fits-all approach may limit the effectiveness of evidence acquisition and downstream answer generation. In this work, we propose an adaptive retrieval framework that selects retrieval and evidence aggregation strategies according to question type. The system combines query understanding, biomedical document retrieval, reranking, knowledge graph augmentation, document clustering, and large language model-based answer generation. For yes/no questions, it focuses on precise evidence retrieval; for factoid and list questions, it emphasizes entity-oriented retrieval and clustering; and for summary questions, it performs broader evidence collection and synthesis. We evaluate the proposed framework on the BioASQ benchmark and demonstrate that adaptive retrieval strategies improve evidence relevance and answer quality across multiple question types. Our results suggest that aligning retrieval mechanisms with question-specific information needs provides an effective direction for enhancing retrieval-augmented biomedical QA systems.
