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.

翻译：网络领域以其高度复杂性和快速迭代为特征，完成网络任务（包括网络设计、诊断、配置和安全）需要丰富的专业知识。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。这些方法通常难以泛化和自动化复杂的网络任务，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再训练来适应新场景。然而，大型语言模型（LLMs）的最新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。这些在大量数据上训练的模型能够惠及网络领域。一些研究已经探索了LLMs在网络领域的应用，并揭示了有前景的结果。通过回顾最近的进展，我们提出了一个抽象的工作流程来描述将LLM应用于网络领域所涉及的基本过程。我们按类别介绍现有工作的亮点，并详细解释它们如何在流程的不同阶段运作。此外，我们探讨了遇到的挑战、讨论了潜在的解决方案，并概述了未来的研究前景。我们希望这篇综述能为研究人员和从业者提供见解，促进这一跨学科研究领域的发展。