  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.
