Large Language Models (LLMs) possess human-level cognitive and decision-making capabilities, making them a key technology for 6G. However, applying LLMs to the communication domain faces three major challenges: 1) Inadequate communication data; 2) Restricted input modalities; and 3) Difficulty in knowledge retrieval. To overcome these issues, we propose CommGPT, a multimodal foundation model designed specifically for communications. First, we create high-quality pretraining and fine-tuning datasets tailored in communication, enabling the LLM to engage in further pretraining and fine-tuning with communication concepts and knowledge. Then, we design a multimodal encoder to understand and process information from various input modalities. Next, we construct a Graph and Retrieval-Augmented Generation (GRG) framework, efficiently coupling Knowledge Graph (KG) with Retrieval-Augmented Generation (RAG) for multi-scale learning. Finally, we demonstrate the feasibility and effectiveness of the CommGPT through experimental validation.
@article{arxiv.2502.18763,
title = {CommGPT: A Graph and Retrieval-Augmented Multimodal Communication Foundation Model},
author = {Feibo Jiang and Wanyun Zhu and Li Dong and Kezhi Wang and Kun Yang and Cunhua Pan and Octavia A. Dobre},
journal= {arXiv preprint arXiv:2502.18763},
year = {2025}
}