With the rapid development of generative AI technologies， especially breakthroughs in the field of large language models （LLMs）， both academia and industry are actively seeking deeper integration between these large-scale AI models and communication networks. This paper aims to explore this emerging field in depth by reviewing the latest research advancements. It provides a comprehensive analysis of how LLMs can enhance the intelligence of communication networks and how communication networks can improve the performance of LLMs. First， the paper introduces the mainstream Transformer-based architectures of LLMs， elaborating on their training processes and the mechanism of intelligent emergence. It then analyzes the intelligent applications of LLMs in network design， diagnostics， configuration， security， network language understanding， and specification analysis， and discusses the corresponding technical implementation methods. Furthermore， the paper explores the crucial role of communication networks in supporting the training， inference， and deployment of LLMs， with a focus on distributed LLM construction technologies based on cloud-edge collaboration and multi-agent LLM network construction solutions. Finally， the paper identifies several key research challenges that remain to be addressed and provides insights into future research directions.