SOURCE-LINKED INTELLIGENCE
Which Metrics Save the Most Human Annotation? Prediction-Powered Evaluation and Meta-Evaluation
Across various non-verifiable tasks, human evaluation is reliable but expensive, while automatic metrics are more scalable but often biased. Building on prediction-powered inference (PPI), we propose prediction-powered evaluation, a framework that combines limited human judgments with large-scale automatic scores to obtain data-efficient system comparisons that are provably unbiased. We develop parametric and non-parametric procedures, analyze the efficiency trade-off between paired and unpaired designs, and validate the framework on six WMT datasets. We further introduce the Prediction-Powere
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T05:48:14.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.