arXiv 2508.082042026-05-28cs.CLcs.AI

大型语言模型中推理时间不确定性的人类对齐与校准

Human-Alignment and Calibration of Inference-Time Uncertainty in Large Language Models

Kyle Moore, Jesse Roberts, Daryl Watson

备注：: We have discovered a critical error in the normalized entropy calculation that may have substantially inflated nearly all results herein. We have since fixed this error in a new work, but we believe that the new work is sufficiently dissimilar in focus, methods, dataset, and results as to be misleading if presented as a simple replacement. As such, we propose removal and retraction instead

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AI总结：

本文评估了多种推理时间不确定性度量，发现它们与人类群体不确定性高度对齐，尽管与人类答案偏好不一致，但在正确性相关性和分布分析上表现出中等到强校准证据。

摘要：

最近，评估大型语言模型的不确定性校准引起了广泛关注，以促进模型控制和调节用户信任。推理时间不确定性可能为模型或外部控制模块提供实时信号，对于应用这些概念以改善LLM用户体验尤为重要。尽管许多现有论文考虑模型校准，但相对较少的工作试图评估模型不确定性与人类不确定性的对齐程度。在这项工作中，我们使用既有度量和新颖变体评估了一系列推理时间不确定性度量，以确定它们与人类群体水平不确定性以及传统模型校准概念的接近程度。我们发现，许多度量显示出与人类不确定性强烈对齐的证据，尽管与人类答案偏好缺乏对齐。对于那些成功的度量，我们在正确性相关性和分布分析方面发现了中等到强校准证据。

英文摘要：

There has been much recent interest in evaluating large language models for uncertainty calibration to facilitate model control and modulate user trust. Inference time uncertainty, which may provide a real-time signal to the model or external control modules, is particularly important for applying these concepts to improve LLM-user experience in practice. While many of the existing papers consider model calibration, comparatively little work has sought to evaluate how closely model uncertainty aligns to human uncertainty. In this work, we evaluate a collection of inference-time uncertainty measures, using both established metrics and novel variations, to determine how closely they align with both human group-level uncertainty and traditional notions of model calibration. We find that numerous measures show evidence of strong alignment to human uncertainty, even despite the lack of alignment to human answer preference. For those successful metrics, we find moderate to strong evidence of model calibration in terms of both correctness correlation and distributional analysis.

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