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arXiv · 2601.17339

Beyond Correlations: A Downstream Evaluation Framework for Query Performance Prediction

Payel Santra·Partha Basuchowdhuri·Debasis Ganguly
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Abstract

The standard practice of query performance prediction (QPP) evaluation is to measure a set-level correlation between the estimated retrieval qualities and the true ones. However, neither this correlation-based evaluation measure quantifies QPP effectiveness at the level of individual queries, nor does this connect to a downstream application, meaning that QPP methods yielding high correlation values may not find a practical application in query-specific decisions in an IR pipeline. In this paper, we propose a downstream-focussed evaluation framework where a distribution of QPP estimates across a list of top-documents retrieved with several rankers is used as priors for IR fusion. While on the one hand, a distribution of these estimates closely matching that of the true retrieval qualities indicates the quality of the predictor, their usage as priors on the other hand indicates a predictor's ability to make informed choices in an IR pipeline. Our experiments firstly establish the importance of QPP estimates in weighted IR fusion, yielding substantial improvements of over 4.5% over unweighted CombSUM and RRF fusion strategies, and secondly, reveal new insights that the downstream effectiveness of QPP does not correlate well with the standard correlation-based QPP evaluation.

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Payel Santra, Partha Basuchowdhuri, Debasis Ganguly. 2026-01-24. Beyond Correlations: A Downstream Evaluation Framework for Query Performance Prediction. https://arxiv.org/abs/2601.17339

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