Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, Fei Huang


Abstract
Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely large pretrained model with a limited training corpus arises in various downstream tasks. In this paper, we propose a straightforward yet effective fine-tuning technique, Child-Tuning, which updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process. Experiments on various downstream tasks in GLUE benchmark show that Child-Tuning consistently outperforms the vanilla fine-tuning by 1.5 8.6 average score among four different pretrained models, and surpasses the prior fine-tuning techniques by 0.6 1.3 points. Furthermore, empirical results on domain transfer and task transfer show that Child-Tuning can obtain better generalization performance by large margins.
Anthology ID:
2021.emnlp-main.749
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9514–9528
Language:
URL:
https://aclanthology.org/2021.emnlp-main.749
DOI:
10.18653/v1/2021.emnlp-main.749
Bibkey:
Cite (ACL):
Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang. 2021. Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9514–9528, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning (Xu et al., EMNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2021.emnlp-main.749.pdf
Video:
 https://preview.aclanthology.org/author-url/2021.emnlp-main.749.mp4
Code
 alibaba/AliceMind +  additional community code
Data
GLUEQNLISICKSNLI