Continual Training of Language Models for Few-Shot Learning

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

Continual Training of Language Models for Few-Shot Learning

Computation and Language 2022-10-12 v1 Artificial Intelligence Machine Learning Neural and Evolutionary Computing

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

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.

Keywords

instruction tuning pre-trained language model cross-lingual transfer

Cite

@article{arxiv.2210.05549,
  title  = {Continual Training of Language Models for Few-Shot Learning},
  author = {Zixuan Ke and Haowei Lin and Yijia Shao and Hu Xu and Lei Shu and Bing Liu},
  journal= {arXiv preprint arXiv:2210.05549},
  year   = {2022}
}

Continual Training of Language Models for Few-Shot Learning

Abstract

Keywords

Cite

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