Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks

TL;DR

This paper introduces a novel approach called 'generative AI-in-the-loop' that combines large language models with traditional machine learning to improve complex network management and security in next-generation mobile networks.

Contribution

It proposes integrating LLMs with ML models for enhanced network operations, analyzing their capabilities, exploring applications, and demonstrating benefits through a case study in intrusion detection.

Findings

LLMs provide semantic understanding and reasoning for network tasks.

Combining LLMs with ML models improves handling of complex network scenarios.

LLMs can generate synthetic data to enhance network security measures.

Abstract

In recent years, machine learning (ML) techniques have created numerous opportunities for intelligent mobile networks and have accelerated the automation of network operations. However, complex network tasks may involve variables and considerations even beyond the capacity of traditional ML algorithms. On the other hand, large language models (LLMs) have recently emerged, demonstrating near-human-level performance in cognitive tasks across various fields. However, they remain prone to hallucinations and often lack common sense in basic tasks. Therefore, they are regarded as assistive tools for humans. In this work, we propose the concept of "generative AI-in-the-loop" and utilize the semantic understanding, context awareness, and reasoning abilities of LLMs to assist humans in handling complex or unforeseen situations in mobile communication networks. We believe that combining LLMs and ML models allows both to leverage their respective capabilities and achieve better results than either model alone. To support this idea, we begin by analyzing the capabilities of LLMs and compare them with traditional ML algorithms. We then explore potential LLM-based applications in line with the requirements of next-generation networks. We further examine the integration of ML and LLMs, discussing how they can be used together in mobile networks. Unlike existing studies, our research emphasizes the fusion of LLMs with traditional ML-driven next-generation networks and serves as a comprehensive refinement of existing surveys. Finally, we provide a case study to enhance ML-based network intrusion detection with synthesized data generated by LLMs. Our case study further demonstrates the advantages of our proposed idea.