探究人类对齐的大型语言模型不确定性

Investigating Human-Aligned Large Language Model Uncertainty

March 16, 2025

作者: Kyle Moore, Jesse Roberts, Daryl Watson, Pamela Wisniewski

cs.AI

摘要

近期研究致力于量化大型语言模型的不确定性，以促进模型调控并调节用户信任。先前的工作主要关注那些具有理论依据或反映模型平均显性行为的不确定性度量。在本研究中，我们探讨了多种不确定性度量方法，旨在识别与人类群体层面不确定性相关联的度量指标。我们发现，贝叶斯度量及一种基于熵的变体——top-k熵，随着模型规模的变化，其表现与人类行为趋于一致。我们还观察到，某些强效度量在模型规模增大时与人类相似性降低，但通过多元线性回归分析，我们发现结合多种不确定性度量能够提供与人类对齐相当的效果，同时减少对模型规模的依赖。

English

Recent work has sought to quantify large language model uncertainty to facilitate model control and modulate user trust. Previous works focus on measures of uncertainty that are theoretically grounded or reflect the average overt behavior of the model. In this work, we investigate a variety of uncertainty measures, in order to identify measures that correlate with human group-level uncertainty. We find that Bayesian measures and a variation on entropy measures, top-k entropy, tend to agree with human behavior as a function of model size. We find that some strong measures decrease in human-similarity with model size, but, by multiple linear regression, we find that combining multiple uncertainty measures provide comparable human-alignment with reduced size-dependency.