发表机构
Indian Statistical Institute; IIT Bombay; IIT Bhilai（印度统计学院; 孟买印度理工学院; 比莱印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过理想扩展查询和可分离性两个视角解释查询扩展性能差异，提出度量方法并在多个TREC数据集上验证，发现接近理想扩展查询的查询效果更好。
AI 中文摘要
查询扩展（QE）技术长期以来被广泛应用于信息检索（IR）中，以解决词汇不匹配问题。在现代检索系统中，包括基于大语言模型（LLMs）的系统，这些技术仍然具有相关性。然而，没有任何一种查询扩展方法能在所有查询中始终优于其他方法。本工作旨在通过两个互补的视角来解释查询扩展性能的差异。第一个视角是理想扩展查询（IEQ）的概念——一个假设的查询，能够在下游BM25检索模型中最大化检索效果。第二个视角是可分离性视角，它使用Cohen's d量化给定扩展查询下相关文档和非相关文档得分的区分程度。我们开发了一种可分离性度量以及实用的公式来近似理想扩展查询，并研究这些因素与检索效果的关系。在TREC Robust集合、TREC DL 2019-2022段落集合以及TREC DL 2019-2020文档集合上的大量实验揭示了几种有趣的模式。特别是，我们发现更接近理想扩展查询的扩展查询往往能获得更高的检索效果。我们进一步表明，相关文档和非相关文档的可分离性为理解查询扩展性能提供了一个互补的视角。
英文摘要
Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem. They remain relevant in modern retrieval systems, including those based on large language models (LLMs). However, no single QE method consistently outperforms others across all queries. This work seeks to explain the variation in QE performance through two complementary perspectives. The first is the concept of an Ideal Expanded Query (IEQ)--a hypothetical query that maximizes retrieval effectiveness with a downstream BM25 retrieval model. The second is a separability perspective, which quantifies how distinctly relevant and non-relevant documents are scored for a given expanded query using Cohen's (d). We develop a separability measure and practical formulations to approximate the IEQ and investigate how these factors relate to retrieval effectiveness. Extensive experiments on the TREC Robust collection, TREC DL 2019-2022 passage collections, and TREC DL 2019-2020 document collections reveal several interesting patterns. In particular, we find that expanded queries that are closer to the ideal expanded query tend to achieve higher retrieval effectiveness. We further show that the separability of relevant and non-relevant documents provides a complementary perspective for understanding QE performance.
