# Query Expansion by Prompting Large Language Models

arXiv: https://arxiv.org/abs/2305.03653 · HTML: https://mlsanity.com/paper/2305.03653/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2023.

## Built on by (most cited first, 30 of 57)

- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Human-inspired Episodic Memory for Infinite Context LLMs](https://mlsanity.com/paper/2407.09450/) (2024)
- [A Survey of Large Language Model Agents for Question Answering](https://mlsanity.com/paper/2503.19213/) (2025)
- [Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators](https://mlsanity.com/paper/2310.07289/) (2023)
- [Teola: Towards End-to-End Optimization of LLM-based Applications](https://mlsanity.com/paper/2407.00326/) (2024)
- [When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets](https://mlsanity.com/paper/2309.08541/) (2023)
- [Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewriting](https://mlsanity.com/paper/2310.08129/) (2023)
- [GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulation](https://mlsanity.com/paper/2404.03746/) (2024)
- [Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases](https://mlsanity.com/paper/2410.14594/) (2024)
- [Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization](https://mlsanity.com/paper/2505.16348/) (2025)
- [GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval](https://mlsanity.com/paper/2310.20158/) (2023)
- [Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM](https://mlsanity.com/paper/2312.15450/) (2023)
- [Context Aware Query Rewriting for Text Rankers using LLM](https://mlsanity.com/paper/2308.16753/) (2023)
- [CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control](https://mlsanity.com/paper/2405.18727/) (2024)
- [ThinkQE: Query Expansion via an Evolving Thinking Process](https://mlsanity.com/paper/2506.09260/) (2025)
- [Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendation](https://mlsanity.com/paper/2410.01598/) (2024)
- [TongSearch-QR: Reinforced Query Reasoning for Retrieval](https://mlsanity.com/paper/2506.11603/) (2025)
- [TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval](https://mlsanity.com/paper/2502.20969/) (2025)
- [Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval](https://mlsanity.com/paper/2308.08285/) (2023)
- [Meta Knowledge for Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2408.09017/) (2024)
- [Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights](https://mlsanity.com/paper/2511.06738/) (2025)
- [KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs](https://mlsanity.com/paper/2506.09542/) (2025)
- [WisPaper: Your AI Scholar Search Engine](https://mlsanity.com/paper/2512.06879/) (2025)
- [Hypothetical Documents or Knowledge Leakage? Rethinking LLM-based Query Expansion](https://mlsanity.com/paper/2504.14175/) (2025)
- [A Little More Like This: Text-to-Image Retrieval with Vision-Language Models Using Relevance Feedback](https://mlsanity.com/paper/2511.17255/) (2025)
- [Think-then-Act: A Dual-Angle Evaluated Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.13050/) (2024)
- [Conversational Query Reformulation with the Guidance of Retrieved Documents](https://mlsanity.com/paper/2407.12363/) (2024)

## 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.