Guest Editorial Special Issue on Generative AI and Large Language Models-Enabled Edge Intelligence

Abstract

The IEEE Networking Letters special issue on Generative AI (GAI) and Large Language Models (LLMs)-enabled Edge Intelligence explores the latest advancements and applications of GAI and LLMs at the network edge, which aim to address the growing demand for intelligence and adaptability driven by the rapid evolution of modern communication systems. Unlike traditional artificial intelligence (AI) technologies, GAI and LLMs possess powerful self-learning, data-modeling, and context-aware capabilities. Specifically, GAI can extract deep features and semantic information from large amounts of data, which allows it to make intelligent predictions and optimize processes for a flexible response to changes in complex environments. Moreover, by simulating network behaviors under different scenarios and generating new data points, GAI can optimize network architecture and expand training datasets to improve model robustness and generalization. Additionally, LLMs can rapidly adapt to diverse tasks through a few-shot learning. Furthermore, they can incorporate techniques such as retrieval-augmented generation (RAG), thereby strengthening their capacity to handle complex text-based tasks and achieve precise information generation and intelligent interaction.