Adapting a Language Model While Preserving its General Knowledge

Zixuan Ke, Yijia Shao, Haowei Lin, Hu Xu, Lei Shu, Bing Liu


Abstract
Domain-adaptive pre-training (or DA-training for short), also known as post-training, aimsto train a pre-trained general-purpose language model (LM) using an unlabeled corpus of aparticular domain to adapt the LM so that end-tasks in the domain can give improved performances. However, existing DA-training methods are in some sense blind as they do not explicitly identify what knowledge in the LM should be preserved and what should be changed by the domain corpus. This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge. Experimental results will demonstrate the effectiveness of the proposed approach.
Anthology ID:
2022.emnlp-main.693
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10177–10188
Language:
URL:
https://aclanthology.org/2022.emnlp-main.693
DOI:
10.18653/v1/2022.emnlp-main.693
Bibkey:
Cite (ACL):
Zixuan Ke, Yijia Shao, Haowei Lin, Hu Xu, Lei Shu, and Bing Liu. 2022. Adapting a Language Model While Preserving its General Knowledge. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10177–10188, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Adapting a Language Model While Preserving its General Knowledge (Ke et al., EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.693.pdf