  Fine-tuned pre-trained language models can suffer from severe miscalibration
for both in-distribution and out-of-distribution (OOD) data due to
over-parameterization. To mitigate this issue, we propose a regularized
fine-tuning method. Our method introduces two types of regularization for
better calibration: (1) On-manifold regularization, which generates pseudo
on-manifold samples through interpolation within the data manifold. Augmented
training with these pseudo samples imposes a smoothness regularization to
improve in-distribution calibration. (2) Off-manifold regularization, which
encourages the model to output uniform distributions for pseudo off-manifold
samples to address the over-confidence issue for OOD data. Our experiments
demonstrate that the proposed method outperforms existing calibration methods
for text classification in terms of expectation calibration error,
misclassification detection, and OOD detection on six datasets. Our code can be
found at https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning.
