ThinkQE: Query Expansion via an Evolving Thinking Process
This work addresses query expansion for web search users by offering an incremental improvement over existing LLM-based methods.
The paper tackles the problem of narrow query expansions in web search by proposing ThinkQE, a framework that uses a thinking-based process and corpus feedback to improve exploration and diversity, achieving consistent performance gains across multiple benchmarks.
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.