Pre-trained language models (PLMs), such as BERT, have achieved outstanding
performance on multiple natural language processing (NLP) tasks. However, such pre …

Intermediate layer matching is shown as an effective approach for improving knowledge
distillation (KD). However, this technique applies matching in the hidden spaces of two …

The success of a pretraining model like BERT in Natural Language Processing (NLP) puts
forward the demand for model compression. Previous works adopting knowledge distillation …

In natural language processing (NLP) tasks, slow inference speed and huge footprints in
GPU usage remain the bottleneck of applying pre-trained deep models in production. As a …

Knowledge distillation (KD) is a highly promising method for mitigating the computational
problems of pre-trained language models (PLMs). Among various KD approaches …

We perform knowledge distillation (KD) benchmark from task-specific BERT-base teacher
models to various student models: BiLSTM, CNN, BERT-Tiny, BERT-Mini, and BERT-Small …

Abstract Knowledge Distillation (KD) is extensively used in Natural Language Processing to
compress the pre-training and task-specific fine-tuning phases of large neural language …

Deep and large pre-trained language models (eg, BERT, GPT-3) are state-of-the-art for
various natural language processing tasks. However, the huge size of these models brings …

Knowledge Distillation (KD) is a model compression algorithm that helps transfer the
knowledge of a large neural network into a smaller one. Even though KD has shown …

To reduce the computational consumption and memory footprint of powerful deep neural
networks for applications on edge devices, many model compression methods have been …