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

Knowledge distillation~(KD) has been proved effective for compressing large-scale pre-
trained language models. However, existing methods conduct KD statically, eg, the student …

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 …

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 …

Large Language Models (LLMs) have showcased exceptional capabilities in various
domains, attracting significant interest from both academia and industry. Despite their …

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

Knowledge Distillation (KD) is a promising technique for reducing the high computational
demand of large language models (LLMs). However, previous KD methods are primarily …

Pre-trained language models have been applied to various NLP tasks with considerable
performance gains. However, the large model sizes, together with the long inference time …

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 is commonly used for compressing neural networks to reduce their
inference cost and memory footprint. However, current distillation methods for auto …