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Recent work like GPT-3 has demonstrated excellent performance of Zero-Shotand Few-Shot learning on many natural language processing (NLP) tasks byscaling up model size, dataset size and the amount of computation. However,training a model like GPT-3 requires huge amount of computational resourceswhich makes it challengeable to researchers. In this work, we propose a methodthat incorporates large-scale distributed training performance into modelarchitecture design. With this method, Yuan 1.0, the current largest singletonlanguage model with 245B parameters, achieves excellent performance onthousands GPUs during training, and the state-of-the-art results on NLP tasks.A data processing method is designed to efficiently filter massive amount ofraw data. The current largest high-quality Chinese corpus with 5TB high qualitytexts is built based on this method. In addition, a calibration and labelexpansion method is proposed to improve the Zero-Shot and Few-Shot performance,and steady improvement is observed on the accuracy of various tasks. Yuan 1.0presents strong capacity of natural language generation, and the generatedarticles are difficult to distinguish from the human-written ones.

Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning