mlsanityData to 14 Oct 2026

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering

arXiv 2502.13361 ↗First posted 19 Feb 2025cs.CL cs.AIcited by 5

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

Abstract (arXiv)

Medical question answering requires extensive access to specialized conceptual knowledge. The current paradigm, Retrieval-Augmented Generation (RAG), acquires expertise medical knowledge through large-scale corpus retrieval and uses this knowledge to guide a general-purpose large language model (LLM) for generating answers. However, existing retrieval approaches often overlook the importance of factual knowledge, which limits the relevance of retrieved conceptual knowledge and restricts its applicability in real-world scenarios, such as clinical decision-making based on Electronic Health Records (EHRs). This paper introduces RGAR, a recurrence generation-augmented retrieval framework that retrieves both relevant factual and conceptual knowledge from dual sources (i.e., EHRs and the corpus), allowing them to interact and refine each another. Through extensive evaluation across three factual-aware medical question answering benchmarks, RGAR establishes a new state-of-the-art performance among medical RAG systems. Notably, the Llama-3.1-8B-Instruct model with RGAR surpasses the considerably larger, RAG-enhanced GPT-3.5. Our findings demonstrate the benefit of extracting factual knowledge for retrieval, which consistently yields improved generation quality.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 45 of its 83 references that are in the map. First posted on arXiv 19 Feb 2025.

Builds on

45 of 83 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions1 Aug
  2. 2024Benchmarking Retrieval-Augmented Generation for Medicine20 Feb

Other references · 43

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2024The Llama 3 Herd of Modelscited by 14,715
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  7. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  8. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  9. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  10. 2020Extracting Training Data from Large Language Modelscited by 2,189
  11. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  12. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
31 more
  1. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
  2. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  3. 2022Compositional Visual Generation with Composable Diffusion Modelscited by 624
  4. 2016Stealing Machine Learning Models via Prediction APIscited by 619
  5. 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 550
  6. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 532
  7. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  8. 2025Search-o1: Agentic Search-Enhanced Large Reasoning Modelscited by 433
  9. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 298
  10. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  11. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 185
  12. 2019PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Textcited by 184
  13. 2024Do Large Language Models Latently Perform Multi-Hop Reasoning?cited by 169
  14. 2024Stealing Part of a Production Language Modelcited by 159
  15. 2024Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Trainingcited by 89
  16. 2021CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction taskscited by 76
  17. 2024MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generationcited by 44
  18. 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 40
  19. 2020Question-Driven Summarization of Answers to Consumer Health Questionscited by 39
  20. 2024EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summariescited by 35
  21. 2024Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Modelscited by 34
  22. 2025Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Homecited by 33
  23. 2024Grounding Language Model with Chunking-Free In-Context Retrievalcited by 30
  24. 2021Rider: Reader-Guided Passage Reranking for Open-Domain Question Answeringcited by 28
  25. 2020CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answeringcited by 26
  26. 2024RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Modelscited by 25
  27. 2022A Thorough Examination on Zero-shot Dense Retrievalcited by 21
  28. 2024To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answeringcited by 15
  29. 2021Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretabilitycited by 10
  30. 2024Reliable Model Watermarking: Defending Against Theft without Compromising on Evasioncited by 9
  31. 2023Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedulecited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answeringcited by 5
  2. 2026TARSE: Test-Time Adaptation via Retrieval of Skills and Experience for Reasoning Agentscited by 4
  3. 2025RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrievalcited by 2
  4. 2026MedCoG: Maximizing LLM Inference Density in Medical Reasoning via Meta-Cognitive Regulationcited by 2
  5. 2025Refine Medical Diagnosis Using Generation Augmented Retrieval and Clinical Practice Guidelinescited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering
  2. 2026A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
  3. 2025From Retrieval to Generation: Unifying External and Parametric Knowledge for Medical Question Answering
  4. 2024Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering
  5. 2025Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework
  6. 2026When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
  7. 2024The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation for Healthcare QA
  8. 2025Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation