Zhen Lin

Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks

2022 · arXiv (Cornell University)

Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated class probabilities. In high-risk applications like healthcare, practitioners require $\textit{fully calibrated}$ probability predictions for decision-making. That is, conditioned on the prediction $\textit{vector}$, $\textit{every}$ class' probability …

Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey

2025

Uncertainty quantification (UQ) enhances the reliability of Large Language Models (LLMs) by estimating confidence in outputs, enabling risk mitigation and selective prediction. However, traditional UQ methods struggle with LLMs due to computational constraints and decoding …