发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对BioASQ第14b任务，构建了结合多源查询扩展等技术的生物医学问答系统，通过弱问题恢复与检索后剪枝提升检索性能，经实验验证可显著优化MAP@10指标。
AI 中文摘要
本研究介绍了DS@GT ARC BioASQ团队构建的生物医学问答流程，整合了多源查询扩展、神经重排序、检索优化及OpenBioLLM辅助的答案生成。该系统将PubMed检索与微调后的基于MiniLM的语义重排序、倒数排名融合（RRF）、基于特征的相关性评分相结合，以提升文献排序质量。针对检索性能较弱的挑战性查询，引入了条件弱问题恢复策略，该策略应用语义扩展、关系感知增强及选择性结果合并；检索后剪枝阶段进一步移除冗余或低相关性片段，同时保留下游答案生成所需的证据覆盖范围。在BioASQ评估批次上的实验结果表明，所提出的恢复与清理策略显著提升了检索鲁棒性及难题集的MAP@10性能；最终系统还纳入输出验证与后处理步骤，以确保BioASQ各阶段的格式一致性与提交可靠性。
英文摘要
This work presents DS@GT ARC BioASQ team's work for a biomedical question answering pipeline, integrating multi-source query expansion, neural reranking, retrieval refinement, and OpenBioLLM-assisted answer generation. The system combines PubMed retrieval with fine-tuned MiniLM-based semantic reranking, Reciprocal Rank Fusion (RRF), and feature-based relevance scoring to improve document ranking quality. To address challenging queries with weak retrieval performance, we introduce a conditional weak-question recovery strategy that applies semantic expansion, relationship-aware augmentation, and selective result merging. A post-retrieval pruning stage further removes redundant or low-relevance snippets while preserving evidence coverage for downstream answer generation. Experimental results on BioASQ evaluation batches demonstrate that the proposed recovery and cleanup strategies substantially improve retrieval robustness and MAP@10 performance on difficult question sets. The final system also incorporates output validation and post-processing steps to ensure formatting consistency and submission reliability across BioASQ phases.
