Systematic Evaluation of Uncertainty Estimation Methods in Large Language Models

• URL: http://arxiv.org/abs/2510.20460v1
• Date: Thu, 23 Oct 2025 11:50:47 GMT
• Title: Systematic Evaluation of Uncertainty Estimation Methods in Large Language Models
• Authors: Christian Hobelsberger, Theresa Winner, Andreas Nawroth, Oliver Mitevski, Anna-Carolina Haensch,
• Abstract summary: We evaluate four approaches for confidence estimation in large language models (LLMs)<n>We conduct experiments on four question-answering tasks using a state-of-the-art open-source LLM.<n>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.
• Score: 1.8374839804848957
• License: http://creativecommons.org/licenses/by/4.0/
• 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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