自动构建领域内示例并用多大模型协作扩写查询，提升搜索效果。
Automatic In-Domain Exemplar Construction and LLM-Based Refinement of Multi-LLM Expansions for Query Expansion
- 用BM25+MonoT5自动挖掘相关文本构建示例池。
- 两阶段多模型协作使检索准确率显著优于传统方法。
- 无需标注数据，适合实际场景的查询扩展任务。
基于大语言模型的查询扩展具有潜力，但通常依赖人工设计提示、手动选取示例或单一模型，难以扩展且易受领域迁移影响。本文提出一种自动化、领域自适应的查询扩展框架：通过BM25-MonoT5流水线挖掘伪相关段落，构建领域内示例池；采用无训练的聚类策略选择多样化示例，实现无需监督的强且稳定的上下文查询扩展。为进一步利用模型互补性，引入双大模型集成机制，两个异构大模型独立生成扩展项，再由一个精炼模型整合为一致结果。在TREC DL20、DBPedia和SciFact数据集上，该优化后的集成方法持续显著优于BM25、Rocchio、零样本及固定少样本基线。该框架提供可复现的示例选择与多模型生成测试平台，是一种实用、无标签的真实查询扩展解决方案。
原文摘要 · Abstract (English)
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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