  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.
