# Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2509.07794 · HTML: https://mlsanity.com/paper/2509.07794/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [Rethinking On-policy Optimization for Query Augmentation](https://mlsanity.com/paper/2510.17139/) (2025)
- [RAGTurk: Best Practices for Retrieval Augmented Generation in Turkish](https://mlsanity.com/paper/2602.03652/) (2026)
- [Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion](https://mlsanity.com/paper/2603.13776/) (2026)
- [Generative Query Expansion with Multilingual LLMs for Cross-Lingual Information Retrieval](https://mlsanity.com/paper/2511.19325/) (2025)
- [Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2606.31692/) (2026)

## Abstract (arXiv)

Modern information retrieval must reconcile short, ambiguous queries with increasingly diverse and dynamic corpora. Query expansion (QE) remains a core technique for mitigating vocabulary mismatch, but its design space has been reshaped by pre-trained and large language models (PLMs/LLMs). This survey reviews QE methods in the PLM/LLM era and provides a unified view of the emerging landscape. We first summarize how different model families enable new expansion behaviors, including stronger contextualization, more controllable generation, and instruction-following. We then organize recent techniques along four complementary design dimensions: where expansion is injected in the pipeline, how it is grounded and interacts with corpus evidence, how it is learned or aligned, and how structured knowledge such as knowledge graphs is incorporated. Beyond taxonomy, we synthesize application patterns and deployment considerations across representative retrieval settings, highlighting practical trade-offs among effectiveness, controllability, grounding quality, and operating cost. Finally, we outline open challenges and future directions toward more reliable, safe, efficient, and continually adaptive QE under real-world constraints.