Automatic In-Domain Exemplar Construction and LLM-Based Refinement of Multi-LLM Expansions for Query Expansion

• URL: http://arxiv.org/abs/2602.08917v1
• Date: Mon, 09 Feb 2026 17:16:39 GMT
• Title: Automatic In-Domain Exemplar Construction and LLM-Based Refinement of Multi-LLM Expansions for Query Expansion
• Authors: Minghan Li, Ercong Nie, Siqi Zhao, Tongna Chen, Huiping Huang, Guodong Zhou,
• Abstract summary: We present an automated, domain-adaptive QE framework that builds in-domain exemplar pools.<n>A training-free cluster-based strategy selects diverse demonstrations, yielding strong and stable in-context QE without supervision.<n>Across TREC DL20, DBPedia, and SciFact, the refined ensemble delivers consistent and statistically significant gains.
• Score: 21.52923294473877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Query expansion with large language models is promising but often relies on hand-crafted prompts, manually chosen exemplars, or a single LLM, making it non-scalable and sensitive to domain shift. We present an automated, domain-adaptive QE framework that builds in-domain exemplar pools by harvesting pseudo-relevant passages using a BM25-MonoT5 pipeline. A training-free cluster-based strategy selects diverse demonstrations, yielding strong and stable in-context QE without supervision. To further exploit model complementarity, we introduce a two-LLM ensemble in which two heterogeneous LLMs independently generate expansions and a refinement LLM consolidates them into one coherent expansion. Across TREC DL20, DBPedia, and SciFact, the refined ensemble delivers consistent and statistically significant gains over BM25, Rocchio, zero-shot, and fixed few-shot baselines. The framework offers a reproducible testbed for exemplar selection and multi-LLM generation, and a practical, label-free solution for real-world QE.

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