Revisiting Query Variants: The Advantage of Retrieval Over Generation of Query Variants for Effective QPP

• URL: http://arxiv.org/abs/2510.02512v1
• Date: Thu, 02 Oct 2025 19:36:58 GMT
• Title: Revisiting Query Variants: The Advantage of Retrieval Over Generation of Query Variants for Effective QPP
• Authors: Fangzheng Tian, Debasis Ganguly, Craig Macdonald,
• Abstract summary: We propose a method that retrieves QVs from a training set for a given target query of QPP.<n>To achieve a high recall in retrieving queries with the most similar information needs, we extend the directly retrieved QVs by a second retrieval.<n>Our experiments, conducted on TREC DL'19 and DL'20, show that the QPP methods with QVs retrieved by our method outperform the best-performing existing generated-QV-based QPP approaches by as much as around 20%.
• Score: 24.439170886636788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging query variants (QVs), i.e., queries with potentially similar information needs to the target query, has been shown to improve the effectiveness of query performance prediction (QPP) approaches. Existing QV-based QPP methods generate QVs facilitated by either query expansion or non-contextual embeddings, which may introduce topical drifts and hallucinations. In this paper, we propose a method that retrieves QVs from a training set (e.g., MS MARCO) for a given target query of QPP. To achieve a high recall in retrieving queries with the most similar information needs as the target query from a training set, we extend the directly retrieved QVs (1-hop QVs) by a second retrieval using their denoted relevant documents (which yields 2-hop QVs). Our experiments, conducted on TREC DL'19 and DL'20, show that the QPP methods with QVs retrieved by our method outperform the best-performing existing generated-QV-based QPP approaches by as much as around 20\%, on neural ranking models like MonoT5.

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