Poster: Could Large Language Models Perform Network Management?

TL;DR

This paper explores the potential of large language models like GPT-4, Llama, and Falcon to improve network management by providing real-time configuration recommendations in complex wireless systems.

Contribution

It benchmarks various LLMs in zero-shot network management tasks, demonstrating their potential for integration into future AI-driven network systems.

Findings

LLMs can provide effective real-time network configuration suggestions.

Benchmark results show promising performance of GPT-4, Llama, and Falcon.

Potential for LLMs to enhance AI-driven network management systems.

Abstract

Modern wireless communication systems have become increasingly complex due to the proliferation of wireless devices, increasing performance standards, and growing security threats. Managing these networks is becoming more challenging, requiring the use of advanced network management methods and tools. AI-driven network management systems such as Self-Optimizing Networks (SONs) are gaining attention. On the other hand, Large Language Models (LLMs) have been demonstrating exceptional zero-shot learning and generalization capabilities across several domains. In this paper, we leverage the potential of LLMs with SONs to enhance future network management systems. Specifically, we benchmark the use of various LLMs such as GPT-4, Llama, and Falcon, in a zero-shot setting based on their real-time network configuration recommendations. Our results indicate promising prospects for integrating LLMs into future network management systems.