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 this https URL

本文提出了一种经过正则化的 fine-tuning 方法，通过在数据流形内插值产生假样本，加强 In-distribution 采样的平滑性；同时通过在采样流形外的假样本输出平均分布的方式来提高 OOD 数据的可靠性，实验证明了该方法的有效性。