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cs.LG2026
From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
Azza Jenane, Nassim Walha, Lukas Kuhn +1
Large Language Models (LLMs) that can express interpretable and calibrated uncertainty are crucial in high-stakes domains. While methods to compute uncertainty post-hoc exist, they…
cs.LG2025★ 1 cited
Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks
Achim Hekler, Lukas Kuhn, Florian Buettner
Reliable uncertainty calibration is essential for safely deploying deep neural networks in high-stakes applications. Deep neural networks are known to exhibit systematic overconfid…