The rapid evolution of network technologies and the growing complexity of network tasks necessitate a paradigm shift in how networks are designed, configured, and managed. With a wealth of knowledge and expertise, large language models (LLMs) are one of the most promising candidates. This paper aims to pave the way for constructing domain-adapted LLMs for networking. Firstly, we present potential LLM applications for vertical network fields and showcase the mapping from natural language to network language. Then, several enabling technologies are investigated, including parameter-efficient finetuning and prompt engineering. The insight is that language understanding and tool usage are both required for network LLMs. Driven by the idea of embodied intelligence, we propose the ChatNet, a domain-adapted network LLM framework with access to various external network tools. ChatNet can reduce the time required for burdensome network planning tasks significantly, leading to a substantial improvement in efficiency. Finally, key challenges and future research directions are highlighted.

翻译：网络技术的快速演进与网络任务日益增长的复杂性，要求我们在网络设计、配置与管理范式上实现变革。凭借丰富的知识与专业能力，大语言模型（LLMs）成为最具潜力的候选技术之一。本文旨在为构建面向网络领域的专用大语言模型铺平道路。首先，我们探讨了大语言模型在垂直网络领域的潜在应用，并展示了从自然语言到网络语言的映射关系。随后，研究了多项支撑技术，包括参数高效微调与提示工程。核心见解在于：网络大语言模型同时需要语言理解能力与工具使用能力。受具身智能理念启发，我们提出了ChatNet——一种可接入多种外部网络工具的领域自适应网络大语言模型框架。ChatNet能够显著缩短繁重网络规划任务所需的时间，从而大幅提升效率。最后，本文强调了关键挑战与未来研究方向。