标题： 重新审视查询变体：检索相对于生成查询变体在有效QPP中的优势 显示英文标题

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

摘要： 利用查询变体（QVs），即与目标查询可能具有相似信息需求的查询，已被证明可以提高查询性能预测（QPP）方法的效果。 现有的基于QV的QPP方法通过查询扩展或非上下文嵌入生成QVs，这可能会引入主题漂移和幻觉。 在本文中，我们提出了一种方法，针对给定的QPP目标查询，从训练集（例如MS MARCO）中检索QVs。 为了从训练集中检索出与目标查询信息需求最相似的查询以实现高召回率，我们通过使用其指定的相关文档进行第二次检索来扩展直接检索到的QVs（1跳QVs），从而得到2跳QVs。 我们的实验在TREC DL'19和DL'20上进行，结果表明，使用我们方法检索到QVs的QPP方法，在像MonoT5这样的神经排序模型上，比现有表现最好的生成QV-based QPP方法提高了多达约20%。 显示英文摘要

摘要： 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.