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Despite achieving state-of-the-art performance on many NLP tasks, the highenergy cost and long inference delay prevent Transformer-based pretrainedlanguage models (PLMs) from seeing broader adoption including for edge andmobile computing. Efficient NLP research aims to comprehensively considercomputation, time and carbon emission for the entire life-cycle of NLP,including data preparation, model training and inference. In this survey, wefocus on the inference stage and review the current state of model compressionand acceleration for pretrained language models, including benchmarks, metricsand methodology.

A Survey on Model Compression and Acceleration for Pretrained Language Models