A new method called prediction-powered smoothing and validation addresses disaggregated AI evaluation by treating the evaluation set as a finite population, since performance varies across domains such as benchmark task types or conversation types in deployed agents. The approach targets the cost of exhaustive testing, which forces evaluation to rest on a sample of labeled units.
Evaluating 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
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