Improved Confidence Estimates for Black-Box Large Language Models

• URL: http://arxiv.org/abs/2608.19323v1
• Date: Wed, 19 Aug 2026 18:00:04 GMT
• Title: Improved Confidence Estimates for Black-Box Large Language Models
• Abstract summary: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs)<n>In this work we show that, by leveraging this dataset, we consistently outperform these existing scores.<n>Our method produces minimal computational overhead, making it a cheap and straightforward enhancement for UQ in LLMs for real-world applications.
• Score: 8.547023487230565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs). Existing methods, from verbalized confidence to ones requiring multiple generations, are often zero-shot and produce scores quantifying uncertainty without the need for labelled data. Nonetheless, in practice one must always evaluate their performance on a dataset of interest before deployment. In this work we show that, by leveraging this dataset, we consistently outperform these existing scores. Specifically, we build simple classifiers that predict LLM response correctness by using these scores and the correctness of similar queries as features. Our method produces minimal computational overhead, making it a cheap and straightforward enhancement for UQ in LLMs for real-world applications.

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