Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation
Researchers propose prediction-powered smoothing (PP-S/PP-TS), Bayesian small-area estimators that improve disaggregated AI evaluation accuracy and validation.
The paper treats AI evaluation sets as finite populations and improves domain-level mean estimates by combining prediction-powered inference with small area estimation. PP-S fits a Bayesian model to each domain's prediction-powered estimate, while PP-TS borrows strength across a reporting taxonomy. A new approximately unbiased design-based cross-validation score selects between direct and smoothed estimators, delivering better point and interval estimates with near-nominal coverage on a curated benchmark and human-graded deployed agent traffic.
- PP-S fits Bayesian models to prediction-powered domain estimates
- PP-TS borrows strength across a reporting taxonomy
- New cross-validation score selects estimators without independent samples
- Near-nominal coverage on curated benchmark and agent traffic
From the original article
1 min read at arxiv.orgEvaluating an AI system requires disaggregated assessment, as performance varies across domains such as benchmark task types or conversation types in deployed agents. Exhaustive testing is expensive, so evaluation rests on a sample of labeled units. We treat the evaluation set as a finite population and seek accurate point and interval estimates of each domain mean.
By Sho Kawano · arxiv.org
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