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Pre-trained models have achieved state-of-the-art results in various NaturalLanguage Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shownthat scaling up pre-trained language models can improve their generalizationabilities. Particularly, the GPT-3 model with 175 billion parameters shows itsstrong task-agnostic zero-shot/few-shot learning capabilities. Despite theirsuccess, these large-scale models are trained on plain texts withoutintroducing knowledge such as linguistic knowledge and world knowledge. Inaddition, most large-scale models are trained in an auto-regressive way. As aresult, this kind of traditional fine-tuning approach demonstrates relativelyweak performance when solving downstream language understanding tasks. In orderto solve the above problems, we propose a unified framework named ERNIE 3.0 forpre-training large-scale knowledge enhanced models. It fuses auto-regressivenetwork and auto-encoding network, so that the trained model can be easilytailored for both natural language understanding and generation tasks withzero-shot learning, few-shot learning or fine-tuning. We trained the model with10 billion parameters on a 4TB corpus consisting of plain texts and alarge-scale knowledge graph. Empirical results show that the model outperformsthe state-of-the-art models on 54 Chinese NLP tasks, and its English versionachieves the first place on the SuperGLUE benchmark (July 3, 2021), surpassingthe human performance by +0.8% (90.6% vs. 89.8%).

ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation