Knowledge distillation (KD) is known as a promising solution to compress large language
models (LLMs) via transferring their knowledge to smaller models. During this process, white …

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

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

Knowledge distillation (KD) is an effective model compression method that can transfer the
internal capabilities of large language models (LLMs) to smaller ones. However, the multi …

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

Despite the success of Large Language Models (LLMs), they still face challenges related to
high inference costs and memory requirements. To address these issues, Knowledge …

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

Recent advancements in large language models (LLMs) have raised concerns about
inference costs, increasing the need for research into model compression. While knowledge …

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

Abstract Knowledge Distillation (KD) has emerged as a promising approach for compressing
large Pre-trained Language Models (PLMs). The performance of KD relies on how to …