Bi-Drop: Generalizable Fine-tuning for Pre-trained Language Models via Adaptive Subnetwork Optimization
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Pretrained language models have achieved remarkable success in a variety of natural language understanding tasks. Nevertheless, finetuning large pretrained models on downstream tasks is susceptible to overfitting if the training set is limited, which will lead to diminished performance. In this work, we propose a dynamic fine-tuning strategy for pretrained language models called Bi-Drop. It utilizes the gradient information of various sub-models generated by dropout to update the model parameters selectively. Experiments on the GLUE benchmark show that Bi-Drop outperforms previous fine-tuning methods by a considerable margin, and exhibits consistent superiority over vanilla fine-tuning across various pretrained models. Furthermore, empirical results indicate that Bi-Drop yields substantial improvements in the multiple task or domain transfer, data imbalance, and low-resource scenarios, demonstrating superb generalization ability and robustness.
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