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arXiv · 2406.11345

Full-ECE: A Metric For Token-level Calibration on Large Language Models

Han Liu·Yupeng Zhang·Bingning Wang·Weipeng Chen·Xiaolin Hu
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Abstract

Deep Neural Networks (DNNs) excel in various domains but face challenges in providing accurate uncertainty estimates, which are crucial for high-stakes applications. Large Language Models (LLMs) have recently emerged as powerful tools, demonstrating exceptional performance in language tasks. However, traditional calibration metrics such as Expected Calibration Error (ECE) and classwise-ECE (cw-ECE) are inadequate for LLMs due to their vast vocabularies, data complexity, and distributional focus. To address this, we propose a novel calibration concept called full calibration and introduce its corresponding metric, Full-ECE. Full-ECE evaluates the entire predicted probability distribution, offering a more accurate and robust measure of calibration for LLMs.

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Han Liu, Yupeng Zhang, Bingning Wang, Weipeng Chen, Xiaolin Hu. 2024-06-17. Full-ECE: A Metric For Token-level Calibration on Large Language Models. https://arxiv.org/abs/2406.11345

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