Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b
On the map: Retrieval-augmented generation → Medical and biomedical RAG
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 19 references that are in the map. First posted on arXiv 2 Aug 2026.
Builds on
11 of 19 references.
Closest ancestors · 2
Same direction, newest first
- 2025Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation12 May
- 2024SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation17 Jun
Other references · 9
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
- 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,234
- 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
- 2020Results of the seventh edition of the BioASQ Challengecited by 28
- 2024OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Modelscited by 23
- 2025Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledgecited by 12
- 2023Bioformer: an efficient transformer language model for biomedical text miningcited by 9
- 2025Can Language Models Critique Themselves? Investigating Self-Feedback for Retrieval Augmented Generation at BioASQ 2025cited by 2
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Citing papers per year: 2026: 1.
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- 2025Beyond Retrieval: Ensembling Cross-Encoders and GPT Rerankers with LLMs for Biomedical QA
- 2026Adaptive Retrieval Strategies for Biomedical Question Answering
- 2026Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study
- 2026Evaluating Biomedical Reranking for LLM-Based Question Answering over Longitudinal Clinical Notes
- 2026A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
- 2024BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A
- 2025Harnessing Collective Intelligence of LLMs for Robust Biomedical QA: A Multi-Model Approach