提升生物医学问答的检索效果，尤其针对难答问题。
Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b
- 用语义扩展和关系增强恢复弱检索问题，提升查询质量。
- 通过神经重排序与融合策略，使文档排序更准确，MAP@10显著提升。
- 适合医疗问答、文献检索等需要高精度的科研场景使用。
本工作介绍DS@GT ARC BioASQ团队在生物医学问答任务中的系统实现，整合多源查询扩展、神经重排序、检索优化及OpenBioLLM辅助的答案生成。系统结合PubMed检索与微调后的MiniLM语义重排序，采用互斥排名融合（RRF）和基于特征的相关性评分，提升文档排序质量。针对检索表现弱的难题，提出条件式弱问题恢复策略，包含语义扩展、关系感知增强与选择性结果合并。后续增加后检索剪枝阶段，去除冗余或低相关片段，同时保留答案生成所需的证据覆盖。在BioASQ评测批次上的实验表明，所提恢复与清理策略显著提升复杂问题集的检索鲁棒性与MAP@10表现。最终系统还包含输出验证与后处理步骤，确保格式一致性与各阶段提交可靠性。
原文摘要 · Abstract (English)
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.
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