  In this paper, we study the problem of uncertainty estimation and calibration
for LLMs. We first formulate the uncertainty estimation problem for LLMs and
then propose a supervised approach that takes advantage of the labeled datasets
and estimates the uncertainty of the LLMs' responses. Based on the formulation,
we illustrate the difference between the uncertainty estimation for LLMs and
that for standard ML models and explain why the hidden neurons of the LLMs may
contain uncertainty information. Our designed approach demonstrates the
benefits of utilizing hidden activations to enhance uncertainty estimation
across various tasks and shows robust transferability in out-of-distribution
settings. We distinguish the uncertainty estimation task from the uncertainty
calibration task and show that a better uncertainty estimation mode leads to a
better calibration performance. Furthermore, our method is easy to implement
and adaptable to different levels of model accessibility including black box,
grey box, and white box.
