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.
@article{arxiv.2406.11345,
title = {Full-ECE: A Metric For Token-level Calibration on Large Language Models},
author = {Han Liu and Yupeng Zhang and Bingning Wang and Weipeng Chen and Xiaolin Hu},
journal= {arXiv preprint arXiv:2406.11345},
year = {2024}
}