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arXiv · 2210.16771

Parameter-Efficient Tuning Makes a Good Classification Head

Zhuoyi Yang·Ming Ding·Yanhui Guo·Qingsong Lv·Jie Tang
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Abstract

In recent years, pretrained models revolutionized the paradigm of natural language understanding (NLU), where we append a randomly initialized classification head after the pretrained backbone, e.g. BERT, and finetune the whole model. As the pretrained backbone makes a major contribution to the improvement, we naturally expect a good pretrained classification head can also benefit the training. However, the final-layer output of the backbone, i.e. the input of the classification head, will change greatly during finetuning, making the usual head-only pretraining (LP-FT) ineffective. In this paper, we find that parameter-efficient tuning makes a good classification head, with which we can simply replace the randomly initialized heads for a stable performance gain. Our experiments demonstrate that the classification head jointly pretrained with parameter-efficient tuning consistently improves the performance on 9 tasks in GLUE and SuperGLUE.

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Zhuoyi Yang, Ming Ding, Yanhui Guo, Qingsong Lv, Jie Tang. 2022-10-30. Parameter-Efficient Tuning Makes a Good Classification Head. https://arxiv.org/abs/2210.16771

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