[2603.27414] Multiple-Prediction-Powered Inference

Mathematics > Statistics Theory arXiv:2603.27414 (math) [Submitted on 28 Mar 2026] Title:Multiple-Prediction-Powered Inference Authors:Charlie Cowen-Breen, Alekh Agarwal, Stephen Bates, William W. Cohen, Jacob Eisenstein, Amir Globerson, Adam Fisch View a PDF of the paper titled Multiple-Prediction-Powered Inference, by Charlie Cowen-Breen and 6 other authors View PDF Abstract:Statistical estimation often involves tradeoffs between expensive, high-quality measurements and a variety of lower-quality proxies. We introduce Multiple-Prediction-Powered Inference (MultiPPI): a general framework for constructing statistically efficient estimates by optimally allocating resources across these diverse data sources. This work provides theoretical guarantees about the minimax optimality, finite-sample performance, and asymptotic normality of the MultiPPI estimator. Through experiments across three diverse large language model (LLM) evaluation scenarios, we show that MultiPPI consistently achieves lower estimation error than existing baselines. This advantage stems from its budget-adaptive allocation strategy, which strategically combines subsets of models by learning their complex cost and correlation structures. Comments: Subjects: Statistics Theory (math.ST); Artificial Intelligence (cs.AI) ACM classes: G.3 Cite as: arXiv:2603.27414 [math.ST]   (or arXiv:2603.27414v1 [math.ST] for this version)   https://doi.org/10.48550/arXiv.2603.27414 Focus to learn more arXiv-issued DOI via Dat...