Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

Understand

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.