When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs
arXiv:2606.30814v1 Announce Type: new 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,
The proliferation of various large language models (LLMs) and their deployment in critical applications necessitates robust and fair evaluation methods, especially as their accuracy and calibration become central concerns.
This research provides a more rigorous framework for comparing LLMs, which is critical for trustworthy AI deployment, regulatory compliance, and informed decision-making in selecting and improving models.
The proposed ACE framework allows for accuracy-controlled evaluation, moving beyond confounded global calibration metrics and enabling fairer cross-model comparisons that account for inherent accuracy differences.
- · AI developers
- · LLM researchers
- · AI ethicists
- · Enterprises deploying AI
- · Models with poor calibration
- · Simplified AI evaluation metrics
The ACE framework will become a standard for evaluating LLM calibration, leading to more nuanced comparisons.
Improved calibration assessment will accelerate the development of more reliable and trustworthy large language models across various applications.
Increased transparency and fairness in AI evaluation could influence AI regulation and public trust in AI systems.
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Read at arXiv cs.CL