Continual Training of Language Models for Few-Shot Learning

作者：Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, Bing Liu · 年份：2022 · DOI：10.18653/v1/2022.emnlp-main.695 · 被引用次数：33 · 研究领域：Domain Adaptation and Few-Shot Learning、Topic Modeling、Multimodal Machine Learning Applications

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 Post-Training), which to our knowledge, is the first continual post-training system. Experimental results verify its effectiveness.