ThinkQE: 通过演进的思考过程实现查询扩展
ThinkQE: Query Expansion via an Evolving Thinking Process
AI总结：
ThinkQE通过演进的思考过程和语料库交互策略，在测试时实现更全面的查询扩展，优于传统密集检索器和重排序器。
摘要：
有效的网络搜索查询扩展受益于促进探索和结果多样性，以捕捉查询的多种解释和方面。尽管最近基于大语言模型的方法提高了检索性能，并在不进行额外训练的情况下展示了强大的领域泛化能力，但它们往往生成狭窄聚焦的扩展，忽视了这些需求。我们提出了ThinkQE，一种测试时查询扩展框架，通过两个关键组件解决这一限制：基于思考的扩展过程，鼓励更深入和全面的语义探索；以及一个与语料库交互的策略，通过从语料库中检索反馈迭代优化扩展。在多样化的网络搜索基准（DL19、DL20和BRIGHT）上的实验表明，ThinkQE在一致性上优于先前的方法，包括训练密集型密集检索器和重排序器。
英文摘要：
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.
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