Dense retrievers in retrieval-augmented generation (RAG) systems exhibit systematic biases -- including brevity, position, literal matching, and repetition biases -- that can compromise retrieval quality. Query rewriting techniques are now standard in RAG pipelines, yet their impact on these biases remains unexplored. We present the first systematic study of how query enhancement techniques affect dense retrieval biases, evaluating five methods across six retrievers. Our findings reveal that simple LLM-based rewriting achieves the strongest aggregate bias reduction (54\%), yet fails under adversarial conditions where multiple biases combine. Mechanistic analysis uncovers two distinct mechanisms: simple rewriting reduces bias through increased score variance, while pseudo-document generation methods achieve reduction through genuine decorrelation from bias-inducing features. However, no technique uniformly addresses all biases, and effects vary substantially across retrievers. Our results provide practical guidance for selecting query enhancement strategies based on specific bias vulnerabilities. More broadly, we establish a taxonomy distinguishing query-document interaction biases from document encoding biases, clarifying the limits of query-side interventions for debiasing RAG systems.

翻译：检索增强生成（RAG）系统中的密集检索器表现出系统性偏置——包括简洁性偏置、位置偏置、字面匹配偏置和重复性偏置——这些偏置可能削弱检索质量。查询重写技术如今已成为RAG流程的标准组件，但其对这些偏置的影响尚未得到探究。我们首次系统研究了查询增强技术如何影响密集检索偏置，在六种检索器上评估了五种方法。研究结果表明，基于简单LLM的重写方法实现了最强的综合偏置降低（54%），但在多种偏置组合的对抗条件下会失效。机制分析揭示了两种不同的机制：简单重写通过提高分数方差来降低偏置，而伪文档生成方法则通过与偏置诱发特征的真实去相关来实现偏置降低。然而，没有任何技术能统一应对所有偏置，且效果在不同检索器间差异显著。我们的结果为根据特定偏置弱点选择查询增强策略提供了实用指导。更广泛而言，我们构建了一个区分查询-文档交互偏置与文档编码偏置的分类框架，阐明了在RAG系统中通过查询侧干预实现去偏置的局限性。