Knowledge distillation (KD) is widely used for compressing a teacher model to reduce its
inference cost and memory footprint, by training a smaller student model. However, current …

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 …

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

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

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

The performance of autoregressive models on natural language generation tasks has
dramatically improved due to the adoption of deep, self-attentive architectures. However …

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

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

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 …