# DMQR-RAG: Diverse Multi-Query Rewriting for RAG

arXiv: https://arxiv.org/abs/2411.13154 · HTML: https://mlsanity.com/paper/2411.13154/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 29 of 29)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://mlsanity.com/paper/2310.03214/) (2023)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [Query Rewriting for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2305.14283/) (2023)
- [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](https://mlsanity.com/paper/2310.06117/) (2023)
- [Query2doc: Query Expansion with Large Language Models](https://mlsanity.com/paper/2303.07678/) (2023)
- [RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation](https://mlsanity.com/paper/2404.00610/) (2024)
- [Learning to Filter Context for Retrieval-Augmented Generation](https://mlsanity.com/paper/2311.08377/) (2023)
- [Searching for Best Practices in Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.01219/) (2024)
- [Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting](https://mlsanity.com/paper/2311.13314/) (2023)
- [DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models](https://mlsanity.com/paper/2403.10081/) (2024)
- [RaFe: Ranking Feedback Improves Query Rewriting for RAG](https://mlsanity.com/paper/2405.14431/) (2024)
- [RAG-Fusion: a New Take on Retrieval-Augmented Generation](https://mlsanity.com/paper/2402.03367/) (2024)
- [TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems](https://mlsanity.com/paper/2311.11315/) (2023)
- [Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach](https://mlsanity.com/paper/2311.13884/) (2023)
- [SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models](https://mlsanity.com/paper/2403.02951/) (2024)
- [RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.12566/) (2024)
- [RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2308.10633/) (2023)

## Built on by (most cited first, 27 of 27)

- [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025)
- [MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.01005/) (2025)
- [InfoAgent: Advancing Autonomous Information-Seeking Agents](https://mlsanity.com/paper/2509.25189/) (2025)
- [OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search](https://mlsanity.com/paper/2604.03675/) (2026)
- [Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction](https://mlsanity.com/paper/2509.16369/) (2025)
- [Annotation-Free Reinforcement Learning Query Rewriting via Verifiable Search Reward](https://mlsanity.com/paper/2507.23242/) (2025)
- [Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2601.15678/) (2026)
- [How You Ask Matters! Adaptive RAG Robustness to Query Variations](https://mlsanity.com/paper/2604.10745/) (2026)
- [ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding](https://mlsanity.com/paper/2607.24743/) (2026)
- [LANCER: LLM Reranking for Nugget Coverage](https://mlsanity.com/paper/2601.22008/) (2026)
- [A Survey of Query Optimization in Large Language Models](https://mlsanity.com/paper/2412.17558/) (2024)
- [Inter-Passage Verification for Multi-evidence Multi-answer QA](https://mlsanity.com/paper/2506.00425/) (2025)
- [SPARC-RAG: Adaptive Sequential-Parallel Scaling with Context Management for Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.00083/) (2026)
- [Long-Context Long-Form Question Answering for Legal Domain](https://mlsanity.com/paper/2602.07190/) (2026)
- [Aligning the Query Space: Greedy Information Projection for Language Model Data Selection](https://mlsanity.com/paper/2603.13790/) (2026)
- [LLM-Oriented Information Retrieval: A Denoising-First Perspective](https://mlsanity.com/paper/2605.00505/) (2026)
- [GPG: Generalized Policy Gradient Theorem for Transformer-based Policies](https://mlsanity.com/paper/2512.10365/) (2025)
- [Query Disambiguation via Answer-Free Context: Doubling Performance on Humanity's Last Exam](https://mlsanity.com/paper/2603.04454/) (2026)
- [FitText: Evolving Agent Tool Ecologies via Memetic Retrieval](https://mlsanity.com/paper/2605.02411/) (2026)
- [BLAgent: Agentic RAG for File-Level Bug Localization](https://mlsanity.com/paper/2605.17965/) (2026)
- [Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking](https://mlsanity.com/paper/2606.01240/) (2026)
- [MPR-CiteG: Enhancing RAG with Multi-Portfolio Retrieval and Citation-Grounded Generation](https://mlsanity.com/paper/2607.22706/) (2026)
- [Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective](https://mlsanity.com/paper/2608.08445/) (2026)
- [Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement](https://mlsanity.com/paper/2608.16628/) (2026)
- [Better Together: Complementary Query Rewriting Under a Strong RAG Baseline](https://mlsanity.com/paper/2609.05637/) (2026)
- [Re-ranking and Late Interaction Drive Retrieval Quality: A Controlled Comparison of RAG Strategies for Scientific Question Answering](https://mlsanity.com/paper/2609.38473/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## 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.