  Calibrating deep neural models plays an important role in building reliable,
robust AI systems in safety-critical applications. Recent work has shown that
modern neural networks that possess high predictive capability are poorly
calibrated and produce unreliable model predictions. Though deep learning
models achieve remarkable performance on various benchmarks, the study of model
calibration and reliability is relatively underexplored. Ideal deep models
should have not only high predictive performance but also be well calibrated.
There have been some recent advances in calibrating deep models. In this
survey, we review the state-of-the-art calibration methods and their principles
for performing model calibration. First, we start with the definition of model
calibration and explain the root causes of model miscalibration. Then we
introduce the key metrics that can measure this aspect. It is followed by a
summary of calibration methods that we roughly classify into four categories:
post-hoc calibration, regularization methods, uncertainty estimation, and
composition methods. We also cover recent advancements in calibrating large
models, particularly large language models (LLMs). Finally, we discuss some
open issues, challenges, and potential directions.
