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-Powered Saving Ratio (PPSR), a meta-metric that measures how much human annotation an automatic metric ...
| Authors | Mingqi Gao, Anthony Sicilia, Weiyan Shi |
|---|---|
| Primary category | cs.CL |
| Published | 27 August 2026 |
| Last updated at source | 30 August 2026 |
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