Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Abstract
Large-scale pre-trained language models have achieved impressive results on a wide range of downstream tasks recently. However, fine-tuning an extremely large-scale pre-trained language model on limited target datasets is often plagued by overfitting and representation degradation. In this paper, we propose a Dynamic Parameter Selection (DPS) algorithm for the large-scale pre-trained models during fine-tuning, which adaptively selects a more promising subnetwork to perform staging updates based on gradients of back-propagation. Experiments on the GLUE benchmark show that DPS outperforms previous fine-tuning methods in terms of overall performance and stability, and consistently achieves better results with variable pre-trained language models. In addition, DPS brings a large magnitude of improvement in out-of-domain transferring experiments and low-resource scenarios, which shows that it can maintain stable general contextual features and reduce the representation collapse. We release our code at https://github.com/ZhangHaojie077/DPS
Cite
@article{arxiv.2211.01642,
title = {Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively},
author = {Haojie Zhang and Ge Li and Jia Li and Zhongjin Zhang and Yuqi Zhu and Zhi Jin},
journal= {arXiv preprint arXiv:2211.01642},
year = {2022}
}
Comments
NeurIPS 2022