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

• URL: http://arxiv.org/abs/2509.22272v1
• Date: Fri, 26 Sep 2025 12:39:10 GMT
• Title: Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
• Authors: Nassim Walha, Sebastian G. Gruber, Thomas Decker, Yinchong Yang, Alireza Javanmardi, Eyke Hüllermeier, Florian Buettner,
• Abstract summary: We introduce Spectral Uncertainty, a novel approach to quantifying and decomposing uncertainties in Large Language Models.<n>Unlike existing baseline methods, our approach incorporates a fine-grained representation of semantic similarity.<n> Empirical evaluations demonstrate that Spectral Uncertainty outperforms state-of-the-art methods in estimating both aleatoric and total uncertainty.
• Score: 32.528332797693984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• ProbFM: Probabilistic Time Series Foundation Model with Uncertainty Decomposition [0.12489632787815884]
  Time Series Foundation Models (TSFMs) have emerged as a promising approach for zero-shot financial forecasting.<n>Current approaches either rely on restrictive distributional assumptions, conflate different sources of uncertainty, or lack principled calibration mechanisms.<n>We present a novel transformer-based probabilistic framework, ProbFM, that leverages Deep Evidential Regression (DER) to provide principled uncertainty quantification.
  arXiv  Detail & Related papers  (2026-01-15T17:02:06Z)
• ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models [23.44710972442814]
  Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet the uncertainty of Large Language Models (LLMs) is non-trivial.<n>We propose a novel grey-box uncertainty quantification method that measures the variation in model outputs before and after semantic-preserving intervention.
  arXiv  Detail & Related papers  (2025-10-15T02:46:43Z)
• Why Machine Learning Models Fail to Fully Capture Epistemic Uncertainty [1.6112718683989882]
  We make use of a more fine-grained taxonomy of epistemic uncertainty sources in machine learning models.<n>We show that high model bias can lead to misleadingly low estimates of epistemic uncertainty.<n>Common second-order uncertainty methods systematically blur bias-induced errors into aleatoric estimates.
  arXiv  Detail & Related papers  (2025-05-29T14:50:46Z)
• Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching [61.73532883992135]
  We propose a new uncertainty-aware stereo matching framework.<n>We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty.
  arXiv  Detail & Related papers  (2024-12-24T23:28:20Z)
• Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings [11.33157177182775]
  Accurately quantifying uncertainty in large language models (LLMs) is crucial for their reliable deployment. Current state-of-the-art methods for measuring semantic uncertainty in LLMs rely on strict bidirectional entailment criteria. We propose a novel approach that leverages semantic embeddings to achieve smoother and more robust estimation of semantic uncertainty.
  arXiv  Detail & Related papers  (2024-10-30T04:41:46Z)
• Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models [42.563558441750224]
  Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. Current methods often struggle to accurately identify, measure, and address the true uncertainty. This paper introduces a comprehensive framework specifically designed to identify and understand the types and sources of uncertainty.
  arXiv  Detail & Related papers  (2024-10-26T15:07:15Z)
• Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities [79.9629927171974]
  Uncertainty in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. We propose Kernel Language Entropy (KLE), a novel method for uncertainty estimation in white- and black-box LLMs.
  arXiv  Detail & Related papers  (2024-05-30T12:42:05Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)
• The Unreasonable Effectiveness of Deep Evidential Regression [72.30888739450343]
  A new approach with uncertainty-aware regression-based neural networks (NNs) shows promise over traditional deterministic methods and typical Bayesian NNs. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a quantification rather than an exact uncertainty.
  arXiv  Detail & Related papers  (2022-05-20T10:10:32Z)
• Non-Linear Spectral Dimensionality Reduction Under Uncertainty [107.01839211235583]
  We propose a new dimensionality reduction framework, called NGEU, which leverages uncertainty information and directly extends several traditional approaches. We show that the proposed NGEU formulation exhibits a global closed-form solution, and we analyze, based on the Rademacher complexity, how the underlying uncertainties theoretically affect the generalization ability of the framework.
  arXiv  Detail & Related papers  (2022-02-09T19:01:33Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.