We present ReFormeR, a pattern-guided approach for query reformulation. Instead of prompting a language model to generate reformulations of a query directly, ReFormeR first elicits short reformulation patterns from pairs of initial queries and empirically stronger reformulations, consolidates them into a compact library of transferable reformulation patterns, and then selects an appropriate reformulation pattern for a new query given its retrieval context. The selected pattern constrains query reformulation to controlled operations such as sense disambiguation, vocabulary grounding, or discriminative facet addition, to name a few. As such, our proposed approach makes the reformulation policy explicit through these reformulation patterns, guiding the LLM towards targeted and effective query reformulations. Our extensive experiments on TREC DL 2019, DL 2020, and DL Hard show consistent improvements over classical feedback methods and recent LLM-based query reformulation and expansion approaches.

翻译：我们提出ReFormeR，一种基于模式的查询重构方法。不同于直接提示语言模型生成查询重构，ReFormeR首先从初始查询与经验性更强的重构查询对中提取简短重构模式，将其整合为可迁移重构模式的紧凑库，然后根据新查询的检索上下文为其选择恰当的重构模式。所选模式将查询重构约束为受控操作，例如语义消歧、词汇锚定或区分性面添加等。通过这种方式，所提出的方法借助这些重构模式使重构策略显式化，引导大语言模型实现精准有效的查询重构。我们在TREC DL 2019、DL 2020和DL Hard上的大量实验表明，该方法相较于经典反馈方法及近期基于LLM的查询重构与扩展方法均取得一致性改进。