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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
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Nassim Walha, Sebastian G. Gruber, Thomas Decker +4
As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A…