ThinkQE: Query Expansion via an Evolving Thinking Process
Yibin Lei, Tao Shen, Andrew Yates

TL;DR
ThinkQE introduces a novel query expansion framework that enhances web search by promoting semantic exploration and iterative refinement, leading to improved retrieval performance across multiple benchmarks.
Contribution
It presents a test-time expansion method combining a thinking process with corpus feedback, addressing the narrow focus of existing LLM-based approaches.
Findings
Outperforms prior methods on DL19, DL20, and BRIGHT benchmarks.
Enhances diversity and exploration in query expansion.
Demonstrates effectiveness without additional training.
Abstract
Effective query expansion for web search benefits from promoting both exploration and result diversity to capture multiple interpretations and facets of a query. While recent LLM-based methods have improved retrieval performance and demonstrate strong domain generalization without additional training, they often generate narrowly focused expansions that overlook these desiderata. We propose ThinkQE, a test-time query expansion framework addressing this limitation through two key components: a thinking-based expansion process that encourages deeper and comprehensive semantic exploration, and a corpus-interaction strategy that iteratively refines expansions using retrieval feedback from the corpus. Experiments on diverse web search benchmarks (DL19, DL20, and BRIGHT) show ThinkQE consistently outperforms prior approaches, including training-intensive dense retrievers and rerankers.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsInformation Retrieval and Search Behavior · Topic Modeling · Web Data Mining and Analysis
