Pre-Trained Models: Past, Present and Future

Xu Han; Zhengyan Zhang; Ning Ding; Yuxian Gu; Xiao Liu; Yuqi Huo; Jiezhong Qiu; Yuan Yao; Ao Zhang; Liang Zhang; Wentao Han; Minlie Huang; Qin Jin; Yanyan Lan; Yang Liu; Zhiyuan Liu; Zhiwu Lu; Xipeng Qiu; Ruihua Song; Jie Tang; Ji-Rong Wen; Jinhui Yuan; Wayne Xin Zhao; Jun Zhu

arXiv · 2106.07139

Pre-Trained Models: Past, Present and Future

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Abstract

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophisticated pre-training objectives and huge model parameters, large-scale PTMs can effectively capture knowledge from massive labeled and unlabeled data. By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks, which has been extensively demonstrated via experimental verification and empirical analysis. It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch. In this paper, we take a deep look into the history of pre-training, especially its special relation with transfer learning and self-supervised learning, to reveal the crucial position of PTMs in the AI development spectrum. Further, we comprehensively review the latest breakthroughs of PTMs. These breakthroughs are driven by the surge of computational power and the increasing availability of data, towards four important directions: designing effective architectures, utilizing rich contexts, improving computational efficiency, and conducting interpretation and theoretical analysis. Finally, we discuss a series of open problems and research directions of PTMs, and hope our view can inspire and advance the future study of PTMs.

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Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, Jun Zhu. 2021-06-14. Pre-Trained Models: Past, Present and Future. https://arxiv.org/abs/2106.07139

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