Aligning Web Query Generation with Ranking Objectives via Direct Preference Optimization

• URL: http://arxiv.org/abs/2505.19307v1
• Date: Sun, 25 May 2025 20:34:12 GMT
• Title: Aligning Web Query Generation with Ranking Objectives via Direct Preference Optimization
• Authors: João Coelho, Bruno Martins, João Magalhães, Chenyan Xiong,
• Abstract summary: We propose a framework that integrates ranking signals into the query generation process.<n> Experiments show higher ranker-assessed relevance between query-document pairs after DPO.
• Score: 21.140086066964667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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