Abstract Knowledge distillation (KD) involves training a small “student” model to replicate the
strong performance of a high-capacity “teacher” model, enabling efficient deployment in …

Abstract Knowledge Distillation (KD) is one of the most effective approaches to deploying
large-scale pre-trained language models in low-latency environments by transferring the …

Despite the advanced intelligence abilities of large language models (LLMs) in various
applications, they still face significant computational and storage demands. Knowledge …

Large-scale language models have recently demonstrated impressive empirical
performance. Nevertheless, the improved results are attained at the price of bigger models …

Knowledge distillation has been shown to be a powerful model compression approach to
facilitate the deployment of pre-trained language models in practice. This paper focuses on …

Knowledge distillation (KD) is the process of transferring knowledge from a large model to a
small one. It has gained increasing attention in the natural language processing community …

Intermediate layer knowledge distillation (KD) can improve the standard KD technique
(which only targets the output of teacher and student models) especially over large pre …

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 widely used for compressing a teacher model to a smaller
student model, reducing its inference cost and memory footprint while preserving model …

Pretrained language models have led to significant performance gains in many NLP tasks.
However, the intensive computing resources to train such models remain an issue …