Improved Confidence Estimates for Black-Box Large Language Models
arXiv:2608.19323v1 Announce Type: new 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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[Submitted on 19 Aug 2026]
Title:Improved Confidence Estimates for Black-Box Large Language Models
View a PDF of the paper titled Improved Confidence Estimates for Black-Box Large Language Models, by Sokhna Diarra Mbacke and 2 other authors
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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.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as: arXiv:2608.19323 [cs.LG]
(or arXiv:2608.19323v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.19323
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gabriel Loaiza-Ganem [view email] [v1] Wed, 19 Aug 2026 18:00:04 UTC (70 KB)
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