ThinkQE: Query Expansion via an Evolving Thinking Process
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914380361236480 |
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| author | Lei, Yibin Shen, Tao Yates, Andrew |
| author_facet | Lei, Yibin Shen, Tao Yates, Andrew |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_09260 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ThinkQE: Query Expansion via an Evolving Thinking Process Lei, Yibin Shen, Tao Yates, Andrew Information Retrieval Computation and Language 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. |
| title | ThinkQE: Query Expansion via an Evolving Thinking Process |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2506.09260 |