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.
@article{arxiv.2210.16771,
title = {Parameter-Efficient Tuning Makes a Good Classification Head},
author = {Zhuoyi Yang and Ming Ding and Yanhui Guo and Qingsong Lv and Jie Tang},
journal= {arXiv preprint arXiv:2210.16771},
year = {2023}
}
Comments
Accepted as a long paper to EMNLP 2022 Main Conference