Query pipeline optimization for cancer patient question answering systems
On the map: Retrieval-augmented generation → Medical and biomedical RAG
Abstract (arXiv)
Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external information and grounding responses in retrieved knowledge. However, query pipeline optimization for cancer patient question-answering (CPQA) systems requires separately optimizing multiple components with domain-specific considerations. We propose a novel three-aspect optimization approach for the RAG query pipeline in CPQA systems, utilizing public biomedical databases like PubMed and PubMed Central. Our optimization includes: (1) document retrieval, utilizing a comparative analysis of NCBI resources and introducing Hybrid Semantic Real-time Document Retrieval (HSRDR); (2) passage retrieval, identifying optimal pairings of dense retrievers and rerankers; and (3) semantic representation, introducing Semantic Enhanced Overlap Segmentation (SEOS) for improved contextual understanding. On a custom-developed dataset tailored for cancer-related inquiries, our optimized RAG approach improved the answer accuracy of Claude-3-haiku by 5.24% over chain-of-thought prompting and about 3% over a naive RAG setup. This study highlights the importance of domain-specific query optimization in realizing the full potential of RAG and provides a robust framework for building more accurate and reliable CPQA systems, advancing the development of RAG-based biomedical systems.
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 12 of its 33 references that are in the map. First posted on arXiv 19 Dec 2024.
Builds on
12 of 33 references.
Closest ancestors · 1
Same direction, newest first
Other references · 11
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2020Measuring Massive Multitask Language Understandingcited by 7,768
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
- 2022Large Language Models Encode Clinical Knowledgecited by 1,807
- 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
- 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,324
- 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
- 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 253
- 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 182
- 2019Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answeringcited by 96
- 2023Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrievalcited by 4
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
- 2025Biomedical Literature Q&A System Using Retrieval-Augmented Generation (RAG)
- 2024The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation for Healthcare QA
- 2025Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation
- 2026Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study
- 2025Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework
- 2025RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
- 2024Rationale-Guided Retrieval Augmented Generation for Medical Question Answering