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Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees

24 May 2025arXiv:2505.18659archive 2025-07-28

Sangwoo Park, Matteo Zecchin, Osvaldo Simeone

Selecting artificial intelligence (AI) models, such as large language models (LLMs), from multiple candidates requires accurate performance estimation. This is ideally achieved through empirical evaluations involving abundant real-world data. However, such evaluations are costly and impractical at scale. To address this challenge, autoevaluation methods leverage synthetic data produced by automated evaluators, such as LLMs-as-judges, reducing variance but potentially introducing bias. Recent approaches have employed semi-supervised prediction-powered inference (\texttt{PPI}) to correct for the bias of autoevaluators. However, the use of autoevaluators may lead in practice to a degradation in sample efficiency compared to conventional methods using only real-world data. In this paper, we propose \texttt{R-AutoEval+}, a novel framework that provides finite-sample reliability guarantees on the model evaluation, while also ensuring an enhanced (or at least no worse) sample efficiency compared to conventional methods. The key innovation of \texttt{R-AutoEval+} is an adaptive construction of the model evaluation variable, which dynamically tunes its reliance on synthetic data, reverting to conventional methods when the autoevaluator is insufficiently accurate. Experiments on the use of LLMs-as-judges for the optimization of quantization settings for the weights of an LLM, and for prompt design in LLMs confirm the reliability and efficiency of \texttt{R-AutoEval+}.

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WSR_PPI_p_value kclip/r_autoeval_plus/testing_by_betting.py official repository ran · fixture could not drive it no licence file found · pointer only · 3fcb0d4ac9f94b5b · report
ppi_money kclip/r_autoeval_plus/testing_by_betting.py official repository ran · honoured contract fingerprinted no licence file found · pointer only · 422a78fcd2b1b6d1 · report
val_p_value_naive kclip/r_autoeval_plus/testing_by_betting.py official repository ran · honoured contract fingerprinted no licence file found · pointer only · 104c30231098a1a1 · report

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