Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Large pre-trained language models (PLMs) have demonstrated strong performance on natural language understanding (NLU) tasks through fine-tuning. However, fine-tuned models still suffer from overconfident predictions, especially in out-of-domain settings. In this paper, we tackle the problem of calibrating finetuned language models. We demonstrate that the PLMs are well-calibrated on the masked language modeling task with robust predictive confidence under domain shift, yet the fine-tuned models fail to retain such property due to catastrophic forgetting, which impacts the calibration on the downstream classification task. In light of these observations, we evaluate the calibration of several methods that preserve pre-trained features and show that preserving pre-trained features can improve the calibration of fine-tuned language models. Among these methods, our proposed method that encourages the fine-tuned model to learn generative representations with auxiliary language modeling objective achieves competitive accuracy and the lowest expected calibration error compared to several strong baselines under both in-domain and out-of-domain settings on three downstream NLU tasks. Fine-tuning pre-trained language models (PLMs) is a dominating paradigm for natural language understanding (NLU) with state-of-the-art results for a variety of NLU tasks (Peters et al., 2018; Devlin et al., 2019; Liu et al., 2019; He et al., 2021a). The powerful fine-tuned language models have been experimented with for decision-making in real-world applications such as the healthcare domain (He et al., 2020) and safety-critical domain (Sandagiri et al., 2020), where the classification networks need to be highly accurate and provide calibrated confidence for their predictions to improve the safety and trustiness of the models (Guo et al., 2017). For example, suppose a medical language inference LM that predicts the disease given the description of symptoms is well-calibrated, i.e., the model's posterior probabilities (or confidence) align well with the true correctness likelihood. In that case, the wrong predictions can be easier to detect and correct by human doctors by given low predictive confidence.