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arXiv 2404.12901cs.NIcs.AI
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面向网络的大型语言模型：工作流、进展与挑战

Large Language Models for Networking: Workflow, Advances and Challenges

Tsinghua University（清华大学）；Peking University（北京大学）

Chang Liu, Xiaohui Xie, Xinggong Zhang, Yong Cui

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AI总结：

本文综述了大型语言模型在网络领域的应用，提出了一个抽象工作流来描述其基本过程，介绍了现有工作的亮点及运作方式，并探讨了面临的挑战、潜在解决方案和未来研究前景。

摘要：

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、诊断到安全的网络任务。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，给基于传统机器学习的方法带来了重大障碍。这些方法通常难以在网络中泛化和自动化复杂任务，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，大型语言模型（LLMs）的最新出现引发了应对这些挑战的新一波可能性。LLMs在自然语言理解、生成和推理方面展现出了卓越的能力。这些在广泛数据上训练出来的模型，可以使网络领域受益。一些努力已经探索了LLMs在网络领域的应用，并显示出有前景的结果。通过回顾最近的进展，我们提出了一个抽象工作流，以描述将LLM应用于网络所涉及的基本过程。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运作方式。此外，我们深入探讨了遇到的挑战，讨论了潜在的解决方案，并概述了未来的研究前景。我们希望本综述能为研究人员和从业者提供洞见，促进这一跨学科研究领域的发展。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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