Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-trained LMs. In this work, we systematically evaluate the impact of the alignment process on logit-based uncertainty calibration of LMs under the multiple-choice setting. We first conduct a thoughtful empirical study on how aligned LMs differ in calibration from their pre-trained counterparts. Experimental results reveal that there are two distinct uncertainties in LMs under the multiple-choice setting, which are responsible for the answer decision and the format preference of the LMs, respectively. Then, we investigate the role of these two uncertainties on aligned LM's calibration through fine-tuning in simple synthetic alignment schemes and conclude that one reason for aligned LMs' overconfidence is the conflation of these two types of uncertainty. Furthermore, we examine the utility of common post-hoc calibration methods for aligned LMs and propose an easy-to-implement and sample-efficient method to calibrate aligned LMs. We hope our findings could provide insights into the design of more reliable alignment processes for LMs.

翻译：尽管对齐语言模型在实际应用中取得了显著进展，但与相应的预训练语言模型相比，它们在输出答案时往往表现出过度自信。本文系统评估了对齐过程对多项选择题设定下基于对数几率的不确定性校准的影响。我们首先通过细致的实证研究，探讨对齐语言模型与预训练模型在校准方面的差异。实验结果表明，在多项选择题设定下，语言模型存在两种不同的不确定性，分别影响模型的答案决策和格式偏好。随后，我们通过简单合成对齐方案下的微调实验，研究了这两种不确定性在对齐语言模型校准中的作用，并得出结论：对齐模型过度自信的原因之一在于这两种不确定性的混淆。此外，我们检验了常见的事后校准方法对于对齐语言模型的有效性，并提出一种易于实现且样本高效的校准方法。我们希望这些发现能为设计更可靠的语言模型对齐过程提供启示。