The networking field is characterized by its high complexity and rapid
iteration, requiring extensive expertise to accomplish network tasks, ranging
from network design, diagnosis, configuration and security. The inherent
complexity of these tasks, coupled with the ever-changing landscape of
networking technologies and protocols, poses significant hurdles for
traditional machine learning-based methods. These methods often struggle to
generalize and automate complex tasks in networking, as they require extensive
labeled data, domain-specific feature engineering, and frequent retraining to
adapt to new scenarios. However, the recent emergence of large language models
(LLMs) has sparked a new wave of possibilities in addressing these challenges.
LLMs have demonstrated remarkable capabilities in natural language
understanding, generation, and reasoning. These models, trained on extensive
data, can benefit the networking domain. Some efforts have already explored the
application of LLMs in the networking domain and revealed promising results. By
reviewing recent advances, we present an abstract workflow to describe the
fundamental process involved in applying LLM for Networking. We introduce the
highlights of existing works by category and explain in detail how they operate
at different stages of the workflow. Furthermore, we delve into the challenges
encountered, discuss potential solutions, and outline future research
prospects. We hope that this survey will provide insight for researchers and
practitioners, promoting the development of this interdisciplinary research
field.

通过回顾现有工作，我们按类别介绍了应用语言模型于网络领域的突出成果，并详细解释它们在工作流程的不同阶段的操作方式。此外，我们深入探讨了遇到的挑战，讨论了潜在解决方案，并勾勒了未来的研究前景。我们希望这份调查能为研究人员和实践者提供洞见，推动该跨学科研究领域的发展。