T hink QE : Query Expansion via an Evolving Thinking Process (2025)

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.

## introduction query expansion (qe) is a common practice in web search scenarios @xcite , particularly for first-stage retrievers such as bm25 @xcite . effective expansion involves not only reinforcing the core intent of the query but also introducing terms that capture different facets or interpretations of the information need. this multifaceted coverage helps capture a broader semantic context, enabling the retrieval of a more comprehensive set of relevant documents. prior studies have shown that such broad-coverage expansion strategies lead to substantial improvements in retrieval quality @xcite . recent advances in large language models (llms) have led to strong performance in query expansion (gao et al., 2022; wang et al., 2023; 1 our code is publicly available at https://github.com/ yibin-lei/think_qe . ## method we introduce thinkqe, a query expansion framework that tightly integrates llm-based thinking process with evolving corpus interaction. thinkqe follows prior work in generating query expansions using retrieved documents but distinguishes itself through its design of thinking augmentation and iterative corpus interactions. the method is designed to enable exploration of the query space through thinking processes and evolving refinement based on retrieval feedback from the corpus. the overall process proceeds in multiple rounds. at each round, an llm performs thinking-augmented expansion based on the original query and newly retrieved documents from the corpus, which in turn informs subsequent retrieval and expansion steps. the following subsections describe each component of the method in detail. ## analysis in this section, we conduct a detailed analysis of thinkqe on the stackexchange domain of the bright benchmark. ## conclusion we presented thinkqe, a test-time query expansion method enhancing exploration and diversity through a thinking-based expansion process and evolving interactions with the corpus. without requiring any training, thinkqe consistently improves retrieval performance across multiple benchmarks by encouraging deeper coverage and adaptive refinement, offering a lightweight yet effective alternative to training-based dense retrievers and rerankers.