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We present an efficient method of pretraining large-scale autoencodinglanguage models using training signals generated by an auxiliary model.Originated in ELECTRA, this training strategy has demonstratedsample-efficiency to pretrain models at the scale of hundreds of millions ofparameters. In this work, we conduct a comprehensive empirical study, andpropose a recipe, namely "Model generated dEnoising TRaining Objective"(METRO), which incorporates some of the best modeling techniques developedrecently to speed up, stabilize, and enhance pretrained language models withoutcompromising model effectiveness. The resultant models, METRO-LM, consisting ofup to 5.4 billion parameters, achieve new state-of-the-art on the GLUE,SuperGLUE, and SQuAD benchmarks. More importantly, METRO-LM are efficient inthat they often outperform previous large models with significantly smallermodel sizes and lower pretraining cost.

METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals