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.

翻译：生物医学问答系统在处理复杂医学查询中发挥着关键作用，然而它们常常因医学数据的复杂性以及对多跳推理的需求而面临挑战。本文提出一种旨在有效处理直接问题与序列问题的模型。对于序列问题，我们将其分解为一系列子问题，以在多个步骤链中进行推理；而对于直接问题，则直接进行处理，以确保效率并最小化处理开销。此外，我们利用多源信息检索与上下文学习技术，为答案生成提供丰富且相关的上下文信息。我们在 BioCreative IX - MedHopQA 共享任务数据集上评估了所提出的模型。该方法取得了 0.84 的精确匹配分数，在当前排行榜上位列第二。这些结果凸显了该模型应对生物医学问答挑战的能力，为推进医学研究与实践提供了一种通用解决方案。