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 …

Knowledge distillation (KD) is widely used to train small, high-performing student language
models (LMs) using large teacher LMs. While effective in fine-tuning, KD during pre-training …

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 …

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

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 …

Teacher-student knowledge distillation is a popular technique for compressing today's
prevailing large language models into manageable sizes that fit low-latency downstream …

In this paper, we propose a novel LLM-Neo framework that efficiently transfers knowledge
from a large language model (LLM) teacher to a compact student. Initially, we revisit the …

Knowledge distillation (KD) is a technique that compresses large teacher models by training
smaller student models to mimic them. The success of KD in auto-regressive language …