Prediction-Powered Inference Across Many Tasks for AI Evaluation & Social Science Research
Many applications require statistically valid inference across many related tasks, while using only a handful of high-quality labels per hypothesis. In AI evaluation, these tasks may correspond to model behaviors across prompts, subgroups, or hypotheses; in social science surveys, they may correspond to related questions, populations, or measurement conditions. Prediction-powered inference (PPI) uses abundant but inexpensive proxy measurements to improve inference from limited, ground-truth labels, but commonly used methods treat tasks independently and therefore fail to exploit shared structure across related tasks. This limitation is especially important in settings where only a small n...
| Authors | Nicolas Emmenegger, Ellery Stahler, Chara Podimata |
|---|---|
| Primary category | stat.ML |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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