Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP) PLMs with hundreds of billionsparameters such as GPT-3 have demonstrated strong performances on naturallanguage understanding and generation with \textit{few-shot in-context}learning . In this work, we present our practice on training large-scaleautoregressive language models named PanGu-$\alpha$ with up to 200 billionparameters . We collect 1.1TB high-quality Chinesedata from a wide range of domains to pretrain the model . We empirically test the generation ability of PanGu$-$$alpha$ in various scenarios including textsummarization, question answering, dialogue generation, etc. Moreover, we investigate the effect of model scales on the few-shot performances across abroad range of Chinese NLP tasks .

Author(s) : Wei Zeng, Xiaozhe Ren, Teng Su, Hui Wang, Yi Liao, Zhiwei Wang, Xin Jiang, ZhenZhang Yang, Kaisheng Wang, Xiaoda Zhang, Chen Li, Ziyan Gong, Yifan Yao, Xinjing Huang, Jun Wang, Jianfeng Yu, Qi Guo, Yue Yu, Yan Zhang, Jin Wang, Hengtao Tao, Dasen Yan, Zexuan Yi, Fang Peng, Fangqing Jiang, Han Zhang, Lingfeng Deng, Yehong Zhang, Zhe Lin, Chao Zhang, Shaojie Zhang, Mingyue Guo, Shanzhi Gu, Gaojun Fan, Yaowei Wang, Xuefeng Jin, Qun Liu, Yonghong Tian

Links : PDF - Abstract

Code :

https://github.com/alsoj/Recommenders-movielens


Coursera

Keywords : language - large - generation - models - pangu -

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