CPM: A large-scale generative Chinese Pre-trained language model

• DOI: 10.1016/j.aiopen.2021.07.001
• Publication Date: 2021-07-15T06:30:27Z
• Authors: Zhengyan Zhang; Xu Han; Hao Zhou; Pei Ke; Yuxian Gu; Deming Ye; Yujia Qin; Yusheng Su; Haozhe Ji; Jian Guan; Fanchao Qi; Xiaozhi Wang; Yanan Zheng; Guoyang Zeng; Huanqi Cao; Shengqi Chen; Daixuan Li; Zhenbo Sun; Zhiyuan Liu; Minlie Huang; Wentao Han; Jie Tang; Juanzi Li; Xiaoyan Zhu; Maosong Sun
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 01 natural sciences; Computation and Language (cs.CL); 0105 earth and related environmental sciences

Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many NLP tasks in the settings of few-shot (even zero-shot) learning. The code and parameters are available at https://github.com/TsinghuaAI/CPM-Generate.