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.

翻译：有效的网络搜索查询扩展需要兼顾探索性与结果多样性，以捕捉查询的多种解释与不同侧面。尽管近期基于大语言模型的方法无需额外训练即可提升检索性能并展现出强大的领域泛化能力，但其生成的扩展往往聚焦过窄，忽略了上述需求。我们提出ThinkQE，一种测试时查询扩展框架，通过两个关键组件解决这一局限：基于思维的扩展过程，以促进更深入全面的语义探索；以及语料库交互策略，利用来自语料库的检索反馈迭代优化扩展。在多样化网络搜索基准（DL19、DL20和BRIGHT）上的实验表明，ThinkQE持续优于现有方法，包括需要密集训练的稠密检索器和重排序器。