Large Language Models for Networking: Applications, Enabling Techniques, and Challenges

TL;DR

This paper explores the development and application of large language models tailored for networking, highlighting their potential to improve network management through domain adaptation, enabling technologies, and tool integration.

Contribution

It introduces the ChatNet framework, a domain-specific network LLM with external tool access, and discusses key challenges and future directions in this field.

Findings

ChatNet significantly reduces network planning time.

Language understanding and tool usage are essential for network LLMs.

Potential applications span various vertical network fields.

Abstract

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.