Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines
Large Language Models (LLMs) have made query reformulation ubiquitous in modern retrieval and Retrieval-Augmented Generation (RAG) pipelines, enabling the generation of multiple semantically equivalent query variants. However, executing the full pipeline for every reformulation is computationally expensive, motivating selective execution: can we identify the best query variant before incurring downstream retrieval and generation costs? We investigate Query Performance Prediction (QPP) as a mechanism for variant selection across ad-hoc retrieval and end-to-end RAG. Unlike traditional QPP, which estimates query difficulty across topics, we study intra-topic discrimination - selecting the op...
| Authors | Negar Arabzadeh, Andrew Drozdov, Michael Bendersky et al. |
|---|---|
| Primary category | cs.IR |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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