@inproceedings{
liu2024ddk,
title={{DDK}: Distilling Domain Knowledge for Efficient Large Language Models},
author={Jiaheng Liu and Chenchen Zhang and Jinyang Guo and Yuanxing Zhang and Haoran Que and Ken Deng and ZhiqiBai and Jie Liu and Ge Zhang and JiakaiWang and Yanan Wu and Congnan Liu and Jiamang Wang and Lin Qu and Wenbo Su and Bo Zheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=xgiurUq0ss}
}
Despite the advanced intelligence abilities of large language models (LLMs) in various applications, they still face significant computational and storage demands.  Knowledge Distillation (KD) has emerged as an effective strategy to improve the performance of a smaller LLM (i.e., the student model) by transferring knowledge from a high-performing LLM (i.e., the teacher model). Prevailing techniques in LLM distillation typically use a black-box model API to generate high-quality pretrained and aligned datasets, or utilize white-box distillation by altering the loss function to better transfer knowledge from the teacher LLM. However, these methods ignore the knowledge differences between the student and teacher LLMs across domains. This results in excessive focus on domains with minimal performance gaps and insufficient attention to domains with large gaps, reducing overall performance. In this paper, we introduce a new LLM distillation framework called DDK, which dynamically adjusts the composition of the distillation dataset in a smooth manner according to the domain performance differences between the teacher and student models, making the distillation process more stable and effective. Extensive evaluations show that DDK significantly improves the performance of student models, outperforming both continuously pretrained baselines and existing knowledge distillation methods by a large margin.
DDK: Distilling Domain Knowledge for Efficient Large Language Models
Jiaheng Liu Chenchen Zhang Jinyang Guo Yuanxing Zhang Haoran Que Ken Deng ZhiqiBai Jie Liu Ge Zhang JiakaiWang Yanan Wu Congnan Liu Jiamang Wang Lin Qu Wenbo Su Bo Zheng