# Generative Query Reformulation for Effective Adhoc Search

arXiv: https://arxiv.org/abs/2308.00415 · HTML: https://mlsanity.com/paper/2308.00415/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2023.

## Built on by (most cited first, 24 of 24)

- [USimAgent: Large Language Models for Simulating Search Users](https://mlsanity.com/paper/2403.09142/) (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)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Context Aware Query Rewriting for Text Rankers using LLM](https://mlsanity.com/paper/2308.16753/) (2023)
- [Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?](https://mlsanity.com/paper/2311.09175/) (2023)
- [Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendation](https://mlsanity.com/paper/2410.01598/) (2024)
- [ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance](https://mlsanity.com/paper/2502.17057/) (2025)
- [Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI](https://mlsanity.com/paper/2510.16720/) (2025)
- [Generative Query Reformulation Using Ensemble Prompting, Document Fusion, and Relevance Feedback](https://mlsanity.com/paper/2405.17658/) (2024)
- [QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation](https://mlsanity.com/paper/2511.15996/) (2025)
- [Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities](https://mlsanity.com/paper/2409.15290/) (2024)
- [Pseudo Relevance Feedback is Enough to Close the Gap Between Small and Large Dense Retrieval Models](https://mlsanity.com/paper/2503.14887/) (2025)
- [InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking](https://mlsanity.com/paper/2506.14086/) (2025)
- [A Reproducibility Study of LLM-Based Query Reformulation](https://mlsanity.com/paper/2604.27421/) (2026)
- [GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval](https://mlsanity.com/paper/2409.10909/) (2024)
- [ReFormeR: Learning and Applying Explicit Query Reformulation Patterns](https://mlsanity.com/paper/2604.01417/) (2026)
- [Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines](https://mlsanity.com/paper/2604.22661/) (2026)
- [ADORE: Iterative Query Expansion with Retrieval-Grounded Relevance Feedback](https://mlsanity.com/paper/2606.13905/) (2026)
- [The Surprising Effectiveness of Rankers Trained on Expanded Queries](https://mlsanity.com/paper/2404.02587/) (2024)
- [QUARK: Robust Retrieval under Non-Faithful Queries via Query-Anchored Aggregation](https://mlsanity.com/paper/2601.21049/) (2026)
- [STORM: Stepwise Token Optimization with Reward-Guided Beam Search](https://mlsanity.com/paper/2606.10621/) (2026)
- [GEM: A Generative Embedding Model Bridging Reasoning and Retrieval](https://mlsanity.com/paper/2608.13200/) (2026)

## Abstract (arXiv)

Performing automatic reformulations of a user's query is a popular paradigm used in information retrieval (IR) for improving effectiveness -- as exemplified by the pseudo-relevance feedback approaches, which expand the query in order to alleviate the vocabulary mismatch problem. Recent advancements in generative language models have demonstrated their ability in generating responses that are relevant to a given prompt. In light of this success, we seek to study the capacity of such models to perform query reformulation and how they compare with long-standing query reformulation methods that use pseudo-relevance feedback. In particular, we investigate two representative query reformulation frameworks, GenQR and GenPRF. GenQR directly reformulates the user's input query, while GenPRF provides additional context for the query by making use of pseudo-relevance feedback information. For each reformulation method, we leverage different techniques, including fine-tuning and direct prompting, to harness the knowledge of language models. The reformulated queries produced by the generative models are demonstrated to markedly benefit the effectiveness of a state-of-the-art retrieval pipeline on four TREC test collections (varying from TREC 2004 Robust to the TREC 2019 Deep Learning). Furthermore, our results indicate that our studied generative models can outperform various statistical query expansion approaches while remaining comparable to other existing complex neural query reformulation models, with the added benefit of being simpler to implement.