QueStER：生成式关键词检索的查询规范
QueStER: Query Specification for Generative keyword-based Retrieval
AI总结：
QueStER通过生成显式的关键词检索规范，结合生成式查询重写和传统检索的效率，提升跨领域检索性能。
摘要：
生成式检索（GR）不同于传统的索引后再检索流程，它通过存储相关性在模型参数中，并直接从查询生成检索提示，但其在领域外可能变得脆弱且扩展成本高。我们引入QueStER（QUEry SpecificaTion for gEnerative Keyword-Based Retrieval），通过学习生成显式的基于关键词的检索规范，将GR与查询改写联系起来。给定一个用户查询，一个轻量级LLM会生成一个关键词查询，该查询由标准检索器（BM25）执行，结合了生成式查询重写的一般化优势与词典索引的效率和可扩展性。我们通过强化学习技术训练重写策略。在领域内和领域外的评估中，QueStER在BM25上持续改进，并与神经检索基线竞争，同时保持强大的效率。
英文摘要：
Generative retrieval (GR) differs from the traditional index-then-retrieve pipeline by storing relevance in model parameters and generating retrieval cues directly from the query, but it can be brittle out of domain and expensive to scale. We introduce QueStER (QUEry SpecificaTion for gEnerative Keyword-Based Retrieval), which bridges GR and query reformulation by learning to generate explicit keyword-based search specifications. Given a user query, a lightweight LLM produces a keyword query that is executed by a standard retriever (BM25), combining the generalization benefits of generative query rewriting with the efficiency and scalability of lexical indexing. We train the rewriting policy with reinforcement learning techniques. Across in- and out-of-domain evaluations, QueStER consistently improves over BM25 and is competitive with neural IR baselines, while maintaining strong efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
