黑盒大语言模型置信度估计的改进方法

AI 摘要

该研究提出一种利用标注数据集改进黑盒大语言模型置信度估计的方法，通过构建简单分类器，将现有不确定性分数和相似查询的正确性作为特征，以预测响应正确性。该方法计算开销小，能持续优于现有零样本不确定性量化方法，并提供校准的置信度估计。

正文节选

Improved Confidence Estimates for Black-Box Large Language Models 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,

发布时间：2026-08-21 12:00

抓取时间：2026-08-21 12:10

来源机构：arXiv