The networking field is characterized by its high complexity and rapid iteration, requiring extensive expertise to accomplish network tasks, ranging from network design, configuration, diagnosis 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.

翻译：网络领域以其高复杂性和快速迭代为特征，需要广泛的专业知识来完成从网络设计、配置、诊断到安全的各种任务。这些任务固有的复杂性，加上网络技术和协议不断变化的格局，给传统的基于机器学习的方法带来了重大障碍。这些方法通常难以推广和自动化网络中的复杂任务，因为它们需要大量的标注数据、特定领域的特征工程以及频繁的重新训练以适应新场景。然而，近期大语言模型（LLMs）的出现为解决这些挑战带来了新的可能性。LLMs在自然语言理解、生成和推理方面展现出了卓越的能力。这些模型通过大量数据训练，可以惠及网络领域。已有一些工作探索了LLMs在网络领域的应用，并展示了令人鼓舞的结果。通过回顾近期进展，我们提出了一个抽象工作流来描述将LLMs应用于网络的基本过程。我们按类别介绍了现有工作的亮点，并详细解释了它们在工作流不同阶段的操作方式。此外，我们深入探讨了所遇到的挑战，讨论了潜在的解决方案，并概述了未来的研究前景。我们希望本综述能为研究者和实践者提供见解，促进这一交叉研究领域的发展。