Towards Explaining Query Expansion Performance in Information Retrieval
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...
| Authors | Sourav Saha, Aditya Dutta, Soumajit Pramanik et al. |
|---|---|
| Primary category | cs.IR |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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