提升生物医学问答的多跳推理与上下文检索能力
UETQuintet at BioCreative IX -- MedHopQA: Enhancing Biomedical QA with Selective Multi-hop Reasoning and Contextual Retrieval
- 区分直接与多跳问题，分别处理以提高效率
- 在BioCreative IX数据集上达到0.84精确匹配得分
- 适合需要精准医学问答的研究者与临床工作者
生物医学问答系统在处理复杂医疗查询时至关重要，但常因医学数据的复杂性及多跳推理需求而受限。本文提出一种模型，能有效应对直接问题与序列式问题：前者直接处理以保证效率，后者通过分解为一系列子问题实现多步推理；同时结合多源信息检索与上下文学习，提供丰富相关背景。在BioCreative IX - MedHopQA共享任务数据集上的评估显示，该模型取得0.84的Exact Match得分，位居当前排行榜第二。结果表明其具备解决生物医学问答挑战的能力，为推动医学研究与实践提供灵活高效的解决方案。
原文摘要 · Abstract (English)
Biomedical Question Answering systems play a critical role in processing complex medical queries, yet they often struggle with the intricate nature of medical data and the demand for multi-hop reasoning. In this paper, we propose a model designed to effectively address both direct and sequential questions. While sequential questions are decomposed into a chain of sub-questions to perform reasoning across a chain of steps, direct questions are processed directly to ensure efficiency and minimise processing overhead. Additionally, we leverage multi-source information retrieval and in-context learning to provide rich, relevant context for generating answers. We evaluated our model on the BioCreative IX - MedHopQA Shared Task datasets. Our approach achieves an Exact Match score of 0.84, ranking second on the current leaderboard. These results highlight the model's capability to meet the challenges of Biomedical Question Answering, offering a versatile solution for advancing medical research and practice.
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