When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs

2026-06-29

AI Summary

This work addresses the susceptibility of existing calibration evaluations for large language models to accuracy disparities, which distorts cross-model comparisons. To enable fair calibration assessment while controlling for accuracy, the authors propose the ACE framework, incorporating three alignment mechanisms: instance alignment, distribution alignment, and candidate alignment. The study systematically reveals, for the first time, the bias inherent in conventional global calibration metrics—such as Expected Calibration Error and Brier Score—when used for comparing models with differing accuracies, and introduces an accuracy-controlled correction strategy. Experimental results demonstrate that the apparent calibration advantages of most models substantially diminish—and their rankings frequently reverse—once calibration metrics are adjusted for accuracy, thereby demonstrating that unadjusted metrics are unsuitable for cross-model calibration evaluation.

Abstract

Calibration evaluates whether a model confidence aligns with its empirical accuracy. Existing studies often compare the calibration of different large language models using global calibration metrics such as Expected Calibration Error and Brier Score. We begin by showing, both theoretically and empirically, that such comparisons are confounded by differences in model accuracy. For fairer cross-model comparison, we then propose ACE, an accuracy-controlled evaluation framework with three complementary views: Instance-Aligned, Distribution-Aligned, and Candidate-Aligned calibration. Across multiple benchmarks, model families, and confidence elicitation methods, we use ACE to study two practically important comparison axes, small versus large models and thinking versus non-thinking models. We find that many previously reported calibration advantages under raw global metrics weaken substantially after accuracy control. We also find that ranking reversal is frequent: models favored by raw metrics often cease to be favored once accuracy is controlled. Our results show that raw global calibration metrics are not robust for cross-model comparison, and that fair calibration comparison requires accuracy-aware evaluation.

Problem

• calibration
• large language models
• model comparison
• accuracy control
• evaluation metrics

Innovation

• calibration
• accuracy-controlled evaluation
• large language models
• ranking reversal
• fair comparison