mlsanityData to 13 Oct 2026

Query Expansion by Prompting Large Language Models

arXiv 2305.03653 ↗First posted 5 May 2023cs.IRcited by 56

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Abstract (arXiv)

Query expansion is a widely used technique to improve the recall of search systems. In this paper, we propose an approach to query expansion that leverages the generative abilities of Large Language Models (LLMs). Unlike traditional query expansion approaches such as Pseudo-Relevance Feedback (PRF) that relies on retrieving a good set of pseudo-relevant documents to expand queries, we rely on the generative and creative abilities of an LLM and leverage the knowledge inherent in the model. We study a variety of different prompts, including zero-shot, few-shot and Chain-of-Thought (CoT). We find that CoT prompts are especially useful for query expansion as these prompts instruct the model to break queries down step-by-step and can provide a large number of terms related to the original query. Experimental results on MS-MARCO and BEIR demonstrate that query expansions generated by LLMs can be more powerful than traditional query expansion methods.

Key facts. Cited by 56 papers in the map (data through 13 Oct 2026). First posted on arXiv 5 May 2023.

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Citing papers per year: 2023: 9; 2024: 18; 2025: 18; 2026: 11.

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  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 353
  2. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  3. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 305
  4. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  5. 2024Human-inspired Episodic Memory for Infinite Context LLMscited by 62
  6. 2025A Survey of Large Language Model Agents for Question Answeringcited by 47
  7. 2023Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generatorscited by 43
  8. 2024Teola: Towards End-to-End Optimization of LLM-based Applicationscited by 32
  9. 2023When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasetscited by 25
  10. 2023Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewritingcited by 23
  11. 2024GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulationcited by 23
  12. 2024Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Basescited by 23
38 more
  1. 2025Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilizationcited by 19
  2. 2023GAR-meets-RAG Paradigm for Zero-Shot Information Retrievalcited by 18
  3. 2023Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLMcited by 18
  4. 2023Context Aware Query Rewriting for Text Rankers using LLMcited by 14
  5. 2024CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Controlcited by 13
  6. 2024Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendationcited by 11
  7. 2025ThinkQE: Query Expansion via an Evolving Thinking Processcited by 11
  8. 2025TongSearch-QR: Reinforced Query Reasoning for Retrievalcited by 11
  9. 2025TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrievalcited by 9
  10. 2023Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrievalcited by 7
  11. 2024Meta Knowledge for Retrieval Augmented Large Language Modelscited by 6
  12. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insightscited by 6
  13. 2025KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphscited by 4
  14. 2025WisPaper: Your AI Scholar Search Enginecited by 4
  15. 2025A Little More Like This: Text-to-Image Retrieval with Vision-Language Models Using Relevance Feedbackcited by 3
  16. 2024Think-then-Act: A Dual-Angle Evaluated Retrieval-Augmented Generationcited by 2
  17. 2024Conversational Query Reformulation with the Guidance of Retrieved Documentscited by 2
  18. 2024RARe: Retrieval Augmented Retrieval with In-Context Examplescited by 2
  19. 2024SRSA: A Cost-Efficient Strategy-Router Search Agent for Real-world Human-Machine Interactionscited by 2
  20. 2025A New Query Expansion Approach via Agent-Mediated Dialogic Inquirycited by 2
  21. 2025A Multi-Agent Perspective on Modern Information Retrievalcited by 2
  22. 2025LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedbackcited by 2
  23. 2025Hypothetical Documents or Knowledge Leakage? Rethinking LLM-based Query Expansioncited by 2
  24. 2025MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answeringcited by 2
  25. 2026RL-Index: Reinforcement Learning for Retrieval Index Reasoningcited by 2
  26. 2024SoftQE: Learned Representations of Queries Expanded by LLMscited by 1
  27. 2024Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrievalcited by 1
  28. 2024A Survey of Query Optimization in Large Language Modelscited by 1
  29. 2025Sherlock Your Queries: Learning to Ask the Right Questions for Dialogue-Based Retrievalcited by 1
  30. 2025NEMO-4-PAYPAL: Leveraging NVIDIA's Nemo Framework for empowering PayPal's Commerce Agentcited by 1
  31. 2026KohakuRAG: A simple RAG framework with hierarchical document indexingcited by 1
  32. 2026Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrievalcited by 1
  33. 2026ReFormeR: Learning and Applying Explicit Query Reformulation Patternscited by 1
  34. 2026MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Rerankingcited by 1
  35. 2024LGDE: Local Graph-based Dictionary Expansioncited by 0
  36. 2024Recursive Abstractive Processing for Retrieval in Dynamic Datasetscited by 0
  37. 2025LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challengecited by 0
  38. 2025Generative Query Expansion with Multilingual LLMs for Cross-Lingual Information Retrievalcited by 0

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  1. 2026Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion
  2. 2025ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
  3. 2025Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion
  4. 2023MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
  5. 2025Rethinking On-policy Optimization for Query Augmentation
  6. 2023Query Rewriting for Retrieval-Augmented Large Language Models
  7. 2026Query Expansion Should Be Coordinated: Dense Expands, Sparse Anchors
  8. 2024Layer-of-Thoughts Prompting (LoT): Leveraging LLM-Based Retrieval with Constraint Hierarchies