Fine-grained Uncertainty Decomposition in Large Language Models: A Spectral Approach

Walha, Nassim; Gruber, Sebastian Gregor; Decker, Thomas; Yang, Yinchong; Javanmardi, Alireza; Hüllermeier, Eyke und Buettner, Florian (14. March 2026): Fine-grained Uncertainty Decomposition in Large Language Models: A Spectral Approach. The 40th Annual AAAI Conference on Artificial Intelligence, Singapore, 20. - 27. January 2026. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 40, No. 31 pp. 26090-26098

Abstract

As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A precise distinction between aleatoric uncertainty, arising from inherent ambiguities within input data, and epistemic uncertainty, originating exclusively from model limitations, is essential to effectively address each uncertainty source. In this paper, we introduce Spectral Uncertainty, a novel approach to quantifying and decomposing uncertainties in LLMs. Leveraging the Von Neumann entropy from quantum information theory, Spectral Uncertainty provides a rigorous theoretical foundation for separating total uncertainty into distinct aleatoric and epistemic components. Unlike existing baseline methods, our approach incorporates a fine-grained representation of semantic similarity, enabling nuanced differentiation among various semantic interpretations in model responses. Empirical evaluations demonstrate that Spectral Uncertainty outperforms state-of-the-art methods in estimating both aleatoric and total uncertainty across diverse models and benchmark datasets.

• Item Type: Conference or Workshop Item (Paper)
• EU Funded Grant Agreement Number: 101088594
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISSN: 2159-5399
• Language: English