mlsanityData to 7 Oct 2026

The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation for Healthcare QA

arXiv 2407.18044 ↗First posted 25 Jul 2024cs.LGcited by 0

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

Deploying Large Language Models (LLMs) for healthcare question answering requires robust methods to ensure accuracy and reliability. This work introduces Query-Based Retrieval Augmented Generation (QB-RAG), a framework for enhancing Retrieval-Augmented Generation (RAG) systems in healthcare question-answering by pre-aligning user queries with a database of curated, answerable questions derived from healthcare content. A key component of QB-RAG is an LLM-based filtering mechanism that ensures that only relevant and answerable questions are included in the database, enabling reliable reference query generation at scale. We provide theoretical motivation for QB-RAG, conduct a comparative analysis of existing retrieval enhancement techniques, and introduce a generalizable, comprehensive evaluation framework that assesses both the retrieval effectiveness and the quality of the generated response based on faithfulness, relevance, and adherence to the guideline. Our empirical evaluation on a healthcare data set demonstrates the superior performance of QB-RAG compared to existing retrieval methods, highlighting its practical value in building trustworthy digital health applications for health question-answering.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 38 references that are in the map. First posted on arXiv 25 Jul 2024.

Builds on

24 of 38 references.

References · 24

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12 more
  1. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
  2. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  3. 2019Document Expansion by Query Predictioncited by 225
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  12. 2024Question-Based Retrieval using Atomic Units for Enterprise RAGcited by 16

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  1. 2024Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering
  2. 2023MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering
  3. 2026A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
  4. 2025Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation
  5. 2025Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework
  6. 2026Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering
  7. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  8. 2024Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions