# Corpus-Steered Query Expansion with Large Language Models

arXiv: https://arxiv.org/abs/2402.18031 · HTML: https://mlsanity.com/paper/2402.18031/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [Large Language Models Struggle to Learn Long-Tail Knowledge](https://mlsanity.com/paper/2211.08411/) (2022)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [Data Contamination: From Memorization to Exploitation](https://mlsanity.com/paper/2203.08242/) (2022)
- [Overview of the TREC 2020 deep learning track](https://mlsanity.com/paper/2102.07662/) (2021)
- [Large language models can accurately predict searcher preferences](https://mlsanity.com/paper/2309.10621/) (2023)
- [When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets](https://mlsanity.com/paper/2309.08541/) (2023)

## Built on by (most cited first, 17 of 17)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Tapered Off-Policy REINFORCE: Stable and efficient reinforcement learning for LLMs](https://mlsanity.com/paper/2503.14286/) (2025)
- [IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generation](https://mlsanity.com/paper/2505.08450/) (2025)
- [KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs](https://mlsanity.com/paper/2506.09542/) (2025)
- [RARe: Retrieval Augmented Retrieval with In-Context Examples](https://mlsanity.com/paper/2410.20088/) (2024)
- [A New Query Expansion Approach via Agent-Mediated Dialogic Inquiry](https://mlsanity.com/paper/2502.08557/) (2025)
- [A Reproducibility Study of LLM-Based Query Reformulation](https://mlsanity.com/paper/2604.27421/) (2026)
- [It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning](https://mlsanity.com/paper/2609.00638/) (2026)
- [A Survey of Query Optimization in Large Language Models](https://mlsanity.com/paper/2412.17558/) (2024)
- [Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.13019/) (2025)
- [RAISE: Enhancing Scientific Reasoning in LLMs via Step-by-Step Retrieval](https://mlsanity.com/paper/2506.08625/) (2025)
- [Seeing Through Words: Controlling Visual Retrieval Quality with Language Models](https://mlsanity.com/paper/2602.21175/) (2026)
- [LGDE: Local Graph-based Dictionary Expansion](https://mlsanity.com/paper/2405.07764/) (2024)
- [Automatic In-Domain Exemplar Construction and LLM-Based Refinement of Multi-LLM Expansions for Query Expansion](https://mlsanity.com/paper/2602.08917/) (2026)
- [FitText: Evolving Agent Tool Ecologies via Memetic Retrieval](https://mlsanity.com/paper/2605.02411/) (2026)
- [Generated Query Expansion Still Helps Strong Sparse Retrieval: A Controlled Study with SPLADE-v3](https://mlsanity.com/paper/2609.37911/) (2026)
- [Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion](https://mlsanity.com/paper/2610.13810/) (2026)

## Abstract (arXiv)

Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documents that answer the queries as expansions. However, challenges arise from misalignments between the expansions and the retrieval corpus, resulting in issues like hallucinations and outdated information due to the limited intrinsic knowledge of LLMs. Inspired by Pseudo Relevance Feedback (PRF), we introduce Corpus-Steered Query Expansion (CSQE) to promote the incorporation of knowledge embedded within the corpus. CSQE utilizes the relevance assessing capability of LLMs to systematically identify pivotal sentences in the initially-retrieved documents. These corpus-originated texts are subsequently used to expand the query together with LLM-knowledge empowered expansions, improving the relevance prediction between the query and the target documents. Extensive experiments reveal that CSQE exhibits strong performance without necessitating any training, especially with queries for which LLMs lack knowledge.