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arXiv · 2510.20460

Systematic Evaluation of Uncertainty Estimation Methods in Large Language Models

Christian Hobelsberger·Theresa Winner·Andreas Nawroth·Oliver Mitevski·Anna-Carolina Haensch
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Abstract

Large language models (LLMs) produce outputs with varying levels of uncertainty, and, just as often, varying levels of correctness; making their practical reliability far from guaranteed. To quantify this uncertainty, we systematically evaluate four approaches for confidence estimation in LLM outputs: VCE, MSP, Sample Consistency, and CoCoA (Vashurin et al., 2025). For the evaluation of the approaches, we conduct experiments on four question-answering tasks using a state-of-the-art open-source LLM. Our results show that each uncertainty metric captures a different facet of model confidence and that the hybrid CoCoA approach yields the best reliability overall, improving both calibration and discrimination of correct answers. We discuss the trade-offs of each method and provide recommendations for selecting uncertainty measures in LLM applications.

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Christian Hobelsberger, Theresa Winner, Andreas Nawroth, Oliver Mitevski, Anna-Carolina Haensch. 2025-10-23. Systematic Evaluation of Uncertainty Estimation Methods in Large Language Models. https://arxiv.org/abs/2510.20460

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