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arXiv · 2505.22169

ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments

Gili Lior·Eliya Habba·Shahar Levy·Avi Caciularu·Gabriel Stanovsky
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Abstract

LLMs are highly sensitive to prompt phrasing, yet standard benchmarks typically report performance using a single prompt, raising concerns about the reliability of such evaluations. In this work, we argue for a stochastic method of moments evaluation over the space of meaning-preserving prompt perturbations. We introduce a formal definition of reliable evaluation that accounts for prompt sensitivity, and suggest ReliableEval - a method for estimating the number of prompt resamplings needed to obtain meaningful results. Using our framework, we stochastically evaluate five frontier LLMs and find that even top-performing models like GPT-4o and Claude-3.7-Sonnet exhibit substantial prompt sensitivity. Our approach is model-, task-, and metric-agnostic, offering a recipe for meaningful and robust LLM evaluation.

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Gili Lior, Eliya Habba, Shahar Levy, Avi Caciularu, Gabriel Stanovsky. 2025-05-28. ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments. https://doi.org/10.18653/v1%2F2025.findings-emnlp.594

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