mlsanityData to 7 Oct 2026

DMQR-RAG: Diverse Multi-Query Rewriting for RAG

arXiv 2411.13154 ↗First posted 20 Nov 2024cs.IR cs.AIcited by 26

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Abstract (arXiv)

Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these issues by incorporating external information. However, user queries frequently contain noise and intent deviations, necessitating query rewriting to improve the relevance of retrieved documents. In this paper, we introduce DMQR-RAG, a Diverse Multi-Query Rewriting framework designed to improve the performance of both document retrieval and final responses in RAG. Specifically, we investigate how queries with varying information quantities can retrieve a diverse array of documents, presenting four rewriting strategies that operate at different levels of information to enhance the performance of baseline approaches. Additionally, we propose an adaptive strategy selection method that minimizes the number of rewrites while optimizing overall performance. Our methods have been rigorously validated through extensive experiments conducted in both academic and industry settings.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 29 of its 39 references that are in the map. First posted on arXiv 20 Nov 2024.

Builds on

29 of 39 references.

References · 29

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  7. 2024Qwen2 Technical Reportcited by 2,076
  8. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  9. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  10. 2023Active Retrieval Augmented Generationcited by 506
  11. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 385
  12. 2023Editing Large Language Models: Problems, Methods, and Opportunitiescited by 367
17 more
  1. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  2. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  3. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 211
  4. 2023Take a Step Back: Evoking Reasoning via Abstraction in Large Language Modelscited by 157
  5. 2023Query2doc: Query Expansion with Large Language Modelscited by 146
  6. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 144
  7. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  8. 2023Learning to Filter Context for Retrieval-Augmented Generationcited by 82
  9. 2023Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofittingcited by 70
  10. 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Modelscited by 63
  11. 2024RaFe: Ranking Feedback Improves Query Rewriting for RAGcited by 47
  12. 2024RAG-Fusion: a New Take on Retrieval-Augmented Generationcited by 42
  13. 2023TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systemscited by 39
  14. 2023Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approachcited by 38
  15. 2024SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Modelscited by 32
  16. 2024RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generationcited by 16
  17. 2023RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Modelscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 18.

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  1. 2025A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challengescited by 58
  2. 2025MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generationcited by 7
  3. 2025InfoAgent: Advancing Autonomous Information-Seeking Agentscited by 7
  4. 2026OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Searchcited by 7
  5. 2025Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reductioncited by 4
  6. 2025Annotation-Free Reinforcement Learning Query Rewriting via Verifiable Search Rewardcited by 3
  7. 2026Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systemscited by 3
  8. 2026How You Ask Matters! Adaptive RAG Robustness to Query Variationscited by 3
  9. 2026LANCER: LLM Reranking for Nugget Coveragecited by 2
  10. 2026ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understandingcited by 2
  11. 2024A Survey of Query Optimization in Large Language Modelscited by 1
  12. 2025Inter-Passage Verification for Multi-evidence Multi-answer QAcited by 1
14 more
  1. 2026SPARC-RAG: Adaptive Sequential-Parallel Scaling with Context Management for Retrieval-Augmented Generationcited by 1
  2. 2026Long-Context Long-Form Question Answering for Legal Domaincited by 1
  3. 2026Aligning the Query Space: Greedy Information Projection for Language Model Data Selectioncited by 1
  4. 2026LLM-Oriented Information Retrieval: A Denoising-First Perspectivecited by 1
  5. 2025GPG: Generalized Policy Gradient Theorem for Transformer-based Policiescited by 0
  6. 2026Query Disambiguation via Answer-Free Context: Doubling Performance on Humanity's Last Examcited by 0
  7. 2026FitText: Evolving Agent Tool Ecologies via Memetic Retrievalcited by 0
  8. 2026BLAgent: Agentic RAG for File-Level Bug Localizationcited by 0
  9. 2026Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunkingcited by 0
  10. 2026MPR-CiteG: Enhancing RAG with Multi-Portfolio Retrieval and Citation-Grounded Generationcited by 0
  11. 2026Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspectivecited by 0
  12. 2026Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinementcited by 0
  13. 2026Re-ranking and Late Interaction Drive Retrieval Quality: A Controlled Comparison of RAG Strategies for Scientific Question Answeringcited by 0
  14. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

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  1. 2026Better Together: Complementary Query Rewriting Under a Strong RAG Baseline
  2. 2026DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation
  3. 2026Sifei at SemEval-2026 Task 8: Hybrid Retrieval and Query Rewriting for Multi-Turn RAG
  4. 2024Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
  5. 2024Optimizing Query Generation for Enhanced Document Retrieval in RAG
  6. 2025LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers
  7. 2025DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization
  8. 2025Investigating the Robustness of Retrieval-Augmented Generation at the Query Level