用偏好优化提升生成查询质量，让搜索效果更好
Aligning Web Query Generation with Ranking Objectives via Direct Preference Optimization
- 用直接偏好优化将排序信号融入查询生成过程
- 在MS-MARCO上下游检索性能显著优于基线模型
- 适合需要高质量合成查询的搜索系统研究者
神经检索模型在网络搜索中表现优异，但其训练需大量标注的查询-文档对，获取成本高。随着ClueWeb22等大规模网页文档集合的普及，大语言模型生成的合成查询成为可扩展的替代方案。然而，合成查询质量参差不齐，导致下游检索效果不佳。现有方法通常依赖外部重排器信号过滤噪声数据。本文提出一种基于直接偏好优化（DPO）的框架，将排序信号直接融入查询生成过程，旨在生成能最大化下游检索效果的高质量查询。实验表明，经DPO优化后，查询与文档间的相关性评分更高，在MS-MARCO基准上的下游性能显著优于使用合成数据训练的基线模型。
原文摘要 · Abstract (English)
Neural retrieval models excel in Web search, but their training requires substantial amounts of labeled query-document pairs, which are costly to obtain. With the widespread availability of Web document collections like ClueWeb22, synthetic queries generated by large language models offer a scalable alternative. Still, synthetic training queries often vary in quality, which leads to suboptimal downstream retrieval performance. Existing methods typically filter out noisy query-document pairs based on signals from an external re-ranker. In contrast, we propose a framework that leverages Direct Preference Optimization (DPO) to integrate ranking signals into the query generation process, aiming to directly optimize the model towards generating high-quality queries that maximize downstream retrieval effectiveness. Experiments show higher ranker-assessed relevance between query-document pairs after DPO, leading to stronger downstream performance on the MS~MARCO benchmark when compared to baseline models trained with synthetic data.
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