mlsanityData to 7 Oct 2026

RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

arXiv 2404.00610 ↗First posted 31 Mar 2024cs.CLcited by 144

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pretraining datasets, making them susceptible to errors in unseen scenarios. To tackle these challenges, Retrieval-Augmented Generation (RAG) addresses this by incorporating external, relevant documents into the response generation process, thus leveraging non-parametric knowledge alongside LLMs' in-context learning abilities. However, existing RAG implementations primarily focus on initial input for context retrieval, overlooking the nuances of ambiguous or complex queries that necessitate further clarification or decomposition for accurate responses. To this end, we propose learning to Refine Query for Retrieval Augmented Generation (RQ-RAG) in this paper, endeavoring to enhance the model by equipping it with capabilities for explicit rewriting, decomposition, and disambiguation. Our experimental results indicate that our method, when applied to a 7B Llama2 model, surpasses the previous state-of-the-art (SOTA) by an average of 1.9% across three single-hop QA datasets, and also demonstrates enhanced performance in handling complex, multi-hop QA datasets. Our code is available at https://github.com/chanchimin/RQ-RAG.

Key facts. Cited by 144 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 36 of its 39 references that are in the map. First posted on arXiv 31 Mar 2024.

Builds on

36 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 35

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  7. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  9. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  10. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  11. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  12. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
23 more
  1. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,114
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  3. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  4. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  5. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  6. 2023LIMA: Less Is More for Alignmentcited by 1,080
  7. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  8. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  9. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
  10. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 754
  11. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 712
  12. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  13. 2023Orca: Progressive Learning from Complex Explanation Traces of GPT-4cited by 369
  14. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  15. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  16. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 253
  17. 2022Quark: Controllable Text Generation with Reinforced Unlearningcited by 236
  18. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 231
  19. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 211
  20. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  21. 2022DocPrompting: Generating Code by Retrieving the Docscited by 151
  22. 2022Recitation-Augmented Language Modelscited by 65
  23. 2023Align on the Fly: Adapting Chatbot Behavior to Established Normscited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 35; 2025: 73; 2026: 36.

144 papers.

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  1. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 310
  2. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 165
  3. 2025ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learningcited by 152
  4. 2024MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentationcited by 118
  5. 2024Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approachcited by 117
  6. 2024InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationalescited by 100
  7. 2024MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Enginescited by 98
  8. 2025VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videoscited by 76
  9. 2024MindSearch: Mimicking Human Minds Elicits Deep AI Searchercited by 65
  10. 2024RS-Agent: Automating Remote Sensing Tasks through Intelligent Agentcited by 46
  11. 2024Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Modelscited by 41
  12. 2025R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learningcited by 32
38 more
  1. 2025Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understandingcited by 30
  2. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generationcited by 28
  3. 2025SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinementcited by 27
  4. 2024Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunicationscited by 26
  5. 2024DMQR-RAG: Diverse Multi-Query Rewriting for RAGcited by 26
  6. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontierscited by 26
  7. 2024Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Modelscited by 25
  8. 2024RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructionscited by 25
  9. 2024Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphscited by 24
  10. 2025TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoningcited by 22
  11. 2025Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foragingcited by 20
  12. 2025Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaborationcited by 19
  13. 2025Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-kcited by 19
  14. 2025Towards AI Search Paradigmcited by 19
  15. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  16. 2025Benchmarking Poisoning Attacks against Retrieval-Augmented Generationcited by 17
  17. 2024RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generationcited by 16
  18. 2024REAPER: Reasoning based Retrieval Planning for Complex RAG Systemscited by 16
  19. 2025IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrievalcited by 16
  20. 2025Hierarchical Document Refinement for Long-context Retrieval-augmented Generationcited by 16
  21. 2024DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answeringcited by 14
  22. 2024OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMscited by 14
  23. 2025Open Data Synthesis For Deep Researchcited by 14
  24. 2025RaCoT: Plug-and-Play Contrastive Example Generation Mechanism for Enhanced LLM Reasoning Reliabilitycited by 14
  25. 2024CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Controlcited by 13
  26. 2025Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applicationscited by 13
  27. 2025ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generationcited by 13
  28. 2024Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generationcited by 11
  29. 2025LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridizationcited by 10
  30. 2025ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answeringcited by 10
  31. 2025RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledgecited by 10
  32. 2024ALTER: Augmentation for Large-Table-Based Reasoningcited by 9
  33. 2024AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systemscited by 9
  34. 2025C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generationcited by 9
  35. 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generationcited by 8
  36. 2024Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coveragecited by 8
  37. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluationcited by 8
  38. 2025DRIFT: Decompose, Retrieve, Illustrate, then Formalize Theoremscited by 8

50 most cited of 144 All 144 (JSON)

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  1. 2025Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
  2. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  3. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  4. 2024RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation
  5. 2025On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems
  6. 2025Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation
  7. 2024Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models
  8. 2024R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation