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    <abstract>Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills. The goal is to improve the few-shot end-task learning in these domains. The resulting system is called CPT (Continual PostTraining), which to our knowledge, is the first continual post-training system. Experimental results verify its effectiveness.</abstract>
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%0 Conference Proceedings
%T Continual Training of Language Models for Few-Shot Learning
%A Ke, Zixuan
%A Lin, Haowei
%A Shao, Yijia
%A Xu, Hu
%A Shu, Lei
%A Liu, Bing
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ke-etal-2022-continual
%X Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills. The goal is to improve the few-shot end-task learning in these domains. The resulting system is called CPT (Continual PostTraining), which to our knowledge, is the first continual post-training system. Experimental results verify its effectiveness.
%R 10.18653/v1/2022.emnlp-main.695
%U https://aclanthology.org/2022.emnlp-main.695
%U https://doi.org/10.18653/v1/2022.emnlp-main.695
%P 10205-10216

Markdown (Informal)

[Continual Training of Language Models for Few-Shot Learning](https://aclanthology.org/2022.emnlp-main.695) (Ke et al., EMNLP 2022)

• Continual Training of Language Models for Few-Shot Learning (Ke et al., EMNLP 2022)

ACL

• Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, and Bing Liu. 2022. Continual Training of Language Models for Few-Shot Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10205–10216, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.