Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively

• URL: http://arxiv.org/abs/2211.01642v1
• Date: Thu, 3 Nov 2022 08:32:12 GMT
• Title: Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
• Authors: Haojie Zhang, Ge Li, Jia Li, Zhongjin Zhang, Yuqi Zhu, Zhi Jin
• Abstract summary: We propose a Dynamic Selection (DPS) algorithm for the large-scale pre-trained models during fine-tuning. Experiments on the GLUE benchmark show that DPS outperforms previous fine-tuning methods in terms of overall performance and stability.
• Score: 32.001304911395756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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