Our paper “Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach” is now available

This work presents Spectral Uncertainty, a new way to decompose uncertainty in large language models. Using the Von Neumann entropy, the method distinguishes aleatoric from epistemic uncertainty and incorporates detailed semantic structure in model outputs. Across multiple benchmarks, it outperforms current approaches in estimating uncertainty.