Large Language Models (LLMs) have showcased exceptional capabilities in various domains, attracting significant interest from both academia and industry. Despite their impressive performance, the substantial size and computational demands of LLMs pose considerable challenges for practical deployment, particularly in environments with limited resources. The endeavor to compress language models while maintaining their accuracy has become a focal point of research. Among the various methods, knowledge distillation has emerged as an effective technique to enhance inference speed without greatly compromising performance. This paper presents a thorough survey from three aspects: method, evaluation, and application, exploring knowledge distillation techniques tailored specifically for LLMs. Specifically, we divide the methods into white-box KD and black-box KD to better illustrate their differences. Furthermore, we also explored the evaluation tasks and distillation effects between different distillation methods, and proposed directions for future research. Through in-depth understanding of the latest advancements and practical applications, this survey provides valuable resources for researchers, paving the way for sustained progress in this field.

这篇论文通过从方法、评估和应用三个方面对专门针对大型语言模型的知识蒸馏技术进行了全面调查，将方法分为白盒知识蒸馏和黑盒知识蒸馏，并探讨了不同蒸馏方法之间的评估任务和蒸馏效果，并提出了未来研究的方向。通过深入了解最新进展和实际应用，这篇综述为研究人员提供了有价值的资源，为这一领域的持续进展铺平了道路。

大型语言模型的知识蒸馏综述：方法、评估与应用