Accounting for Stochasticity in Studies of Large Language Model Refusal

Emma Lurie; Stephanie T. Wang; Sorelle A. Friedler; Danaé Metaxa

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arXiv · 2609.33743

Accounting for Stochasticity in Studies of Large Language Model Refusal

Emma Lurie·Stephanie T. Wang·Sorelle A. Friedler·Danaé Metaxa

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Abstract

We present preliminary empirical evidence that single-observation queries are insufficient for evaluations of LLM refusal behaviors. Using a longitudinal auditing system, we issued identical prompts 100 times each across four dates to GPT-4.1 for two socially salient topics across 20 Wikipedia sources. Refusal outcomes were consistent with a stable Bernoulli process, yet 20\% of sources fell within a decision-boundary region where a single query is largely uninformative. Reliable quantification of refusals required between 15 and 25 repeated queries, well above the single-observation standard common in existing evaluations.

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Emma Lurie, Stephanie T. Wang, Sorelle A. Friedler, Danaé Metaxa. 2026-09-27. Accounting for Stochasticity in Studies of Large Language Model Refusal. https://arxiv.org/abs/2609.33743

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