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

• URL: http://arxiv.org/abs/2505.22169v1
• Date: Wed, 28 May 2025 09:40:48 GMT
• Title: ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments
• Authors: Gili Lior, Eliya Habba, Shahar Levy, Avi Caciularu, Gabriel Stanovsky,
• Abstract summary: We argue for a method of moments evaluation over the space of meaning-preserving prompt perturbations.<n>We show that even top-performing models like GPT-4o and Claude-3.7-Sonnet exhibit substantial prompt sensitivity.
• Score: 21.37415398600286
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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