Single-Query Black-Box Calibration Auditing via Logit Bias

• URL: http://arxiv.org/abs/2609.05125v1
• Date: Fri, 04 Sep 2026 13:24:52 GMT
• Title: Single-Query Black-Box Calibration Auditing via Logit Bias
• Abstract summary: We demonstrate that any API exposing a logit_bias parameter can be mathematically manipulated to evaluate exact probability thresholds.<n>We introduce a novel and provably consistent estimator of the True Error for binary tasks.
• Score: 6.958336861495603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating the calibration of Large Language Models (LLMs) is critical for their safe deployment as zero-shot classifiers. Yet, commercial API providers increasingly hide the continuous output probabilities required by standard calibration metrics. To bypass this opacity, we demonstrate that any LLM API exposing a logit\_bias parameter can be mathematically manipulated to evaluate exact probability thresholds using strictly one query per sample. Leveraging this mechanism, we introduce a novel and provably consistent estimator of the True Calibration Error for binary tasks. Our approach therefore provides an efficient framework for auditing black-box foundation models.

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