用检索反馈生成风格匹配的查询重写数据，提升领域检索效果
ReFeed: Retrieval Feedback-Guided Dataset Construction for Style-Aware Query Rewriting
- 通过检索失败案例自动识别并生成风格匹配的查询重写对
- 在真实数据分布下显著提升检索准确率，验证了风格对齐的有效性
- 适合构建领域自适应的RAG系统或查询重写模型的研究者
检索系统在用户查询与文档语言风格或语义不一致时表现不佳。查询重写被提出以弥合这一差距，通过将用户查询改写为语义等价形式来改善检索效果。然而，现有方法普遍忽视目标文档的风格特征——如领域特定表述、语气和结构——这些特征对匹配真实数据分布至关重要。本文提出一种基于检索反馈的数据集生成框架：自动识别检索失败案例，利用大语言模型将查询重写为相关文档的风格，并通过重新检索验证改进效果。由此构建的（原始，重写）查询对数据集，可训练出显式感知文档风格与检索反馈的重写模型。该工作开辟了数据驱动信息检索的新方向，强调反馈循环与文档风格对齐如何增强RAG系统在真实、领域特定场景中的推理与适应能力。
原文摘要 · Abstract (English)
Retrieval systems often fail when user queries differ stylistically or semantically from the language used in domain documents. Query rewriting has been proposed to bridge this gap, improving retrieval by reformulating user queries into semantically equivalent forms. However, most existing approaches overlook the stylistic characteristics of target documents-their domain-specific phrasing, tone, and structure-which are crucial for matching real-world data distributions. We introduce a retrieval feedback-driven dataset generation framework that automatically identifies failed retrieval cases, leverages large language models to rewrite queries in the style of relevant documents, and verifies improvement through re-retrieval. The resulting corpus of (original, rewritten) query pairs enables the training of rewriter models that are explicitly aware of document style and retrieval feedback. This work highlights a new direction in data-centric information retrieval, emphasizing how feedback loops and document-style alignment can enhance the reasoning and adaptability of RAG systems in real-world, domain-specific contexts.
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