mlsanityData to 14 Oct 2026

Query2doc: Query Expansion with Large Language Models

arXiv 2303.07678 ↗First posted 14 Mar 2023cs.IR cs.CLcited by 149

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Cited by 51 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems. The proposed method first generates pseudo-documents by few-shot prompting large language models (LLMs), and then expands the query with generated pseudo-documents. LLMs are trained on web-scale text corpora and are adept at knowledge memorization. The pseudo-documents from LLMs often contain highly relevant information that can aid in query disambiguation and guide the retrievers. Experimental results demonstrate that query2doc boosts the performance of BM25 by 3% to 15% on ad-hoc IR datasets, such as MS-MARCO and TREC DL, without any model fine-tuning. Furthermore, our method also benefits state-of-the-art dense retrievers in terms of both in-domain and out-of-domain results.

Key facts. Cited by 149 papers in the map (data through 14 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 14 Mar 2023.

Builds on

24 of 37 references.

References · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  6. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  7. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  8. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  9. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  10. 2020Fact or Fiction: Verifying Scientific Claimscited by 554
  11. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  12. 2020RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answeringcited by 413
12 more
  1. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 298
  2. 2020Overview of the TREC 2019 deep learning trackcited by 286
  3. 2021SPLADE: Sparse Lexical and Expansion Model for First Stage Rankingcited by 286
  4. 2019Document Expansion by Query Predictioncited by 231
  5. 2021Overview of the TREC 2020 deep learning trackcited by 196
  6. 2021RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-rankingcited by 176
  7. 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
  8. 2022SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrievalcited by 90
  9. 2021Adversarial Retriever-Ranker for dense text retrievalcited by 83
  10. 2021A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniquescited by 69
  11. 2022Recitation-Augmented Language Modelscited by 65
  12. 2022Query Expansion Using Contextual Clue Sampling with Language Modelscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 20; 2024: 42; 2025: 37; 2026: 50.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2023Cognitive Architectures for Language Agentscited by 429
  3. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 367
  4. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
  5. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 335
  6. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  7. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 307
  8. 2023Improving Text Embeddings with Large Language Modelscited by 303
  9. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  10. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  11. 2024TableRAG: Million-Token Table Understanding with Language Modelscited by 41
  12. 2023RETA-LLM: A Retrieval-Augmented Large Language Model Toolkitcited by 40
38 more
  1. 2024APEER: Automatic Prompt Engineering Enhances Large Language Model Rerankingcited by 28
  2. 2024DMQR-RAG: Diverse Multi-Query Rewriting for RAGcited by 27
  3. 2023Large Language Models are Strong Zero-Shot Retrievercited by 26
  4. 2023When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasetscited by 26
  5. 2024Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Basescited by 23
  6. 2025Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relationscited by 23
  7. 2024A Survey of Conversational Searchcited by 22
  8. 2024Atomic Fact Decomposition Helps Attributed Question Answeringcited by 21
  9. 2025Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilizationcited by 19
  10. 2023Large Search Model: Redefining Search Stack in the Era of LLMscited by 18
  11. 2023GAR-meets-RAG Paradigm for Zero-Shot Information Retrievalcited by 18
  12. 2023Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLMcited by 18
  13. 2024Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrievalcited by 17
  14. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  15. 2023Context Aware Query Rewriting for Text Rankers using LLMcited by 14
  16. 2023Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?cited by 12
  17. 2024Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generationcited by 12
  18. 2024Human-inspired Perspectives: A Survey on AI Long-term Memorycited by 12
  19. 2023Synergistic Interplay between Search and Large Language Models for Information Retrievalcited by 11
  20. 2024Retrieval-Enhanced Machine Learning: Synthesis and Opportunitiescited by 11
  21. 2024Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendationcited by 11
  22. 2025RARE: Retrieval-Augmented Reasoning Modelingcited by 11
  23. 2025QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errorscited by 11
  24. 2024MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Callingcited by 10
  25. 2024No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Userscited by 9
  26. 2025Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generationcited by 9
  27. 2025Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systemscited by 9
  28. 2024Synthetic Data Generation with Large Language Models for Personalized Community Question Answeringcited by 8
  29. 2025DRIFT: Decompose, Retrieve, Illustrate, then Formalize Theoremscited by 8
  30. 2025Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AIcited by 8
  31. 2023Query Understanding in the Age of Large Language Modelscited by 7
  32. 2023Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrievalcited by 7
  33. 2024TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questionscited by 7
  34. 2026AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completioncited by 7
  35. 2023Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisationcited by 6
  36. 2024DocTabQA: Answering Questions from Long Documents Using Tablescited by 6
  37. 2025Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinkingcited by 6
  38. 2024Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approachcited by 5

50 most cited of 149 All 149 (JSON)