The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices. This transition to ubiquitous intelligence demands high connectivity, synchronicity, and end-to-end communication between users and network operators, and will pave the way towards full network automation without human intervention. Intent-based networking is a key factor in the reduction of human actions, roles, and responsibilities while shifting towards novel extraction and interpretation of automated network management. This paper presents the development of a custom Large Language Model (LLM) for 5G and next-generation intent-based networking and provides insights into future LLM developments and integrations to realize end-to-end intent-based networking for fully automated network intelligence.

翻译：将机器学习与人工智能（ML/AI）集成到第五代（5G）网络中，已凸显出网络智能的局限性，其对当前及下一代设备的要求日益严苛且繁重。向泛在智能的转型需要用户与网络运营商之间具备高连接性、同步性及端到端通信能力，并将为实现无需人工干预的完全网络自动化铺平道路。基于意图的网络是减少人工操作、角色与责任，同时转向自动化网络管理的新型提取与解释方式的关键因素。本文介绍了一种为5G及下一代基于意图的网络定制开发的大型语言模型（LLM），并展望了未来LLM的发展与集成，以实现面向全自动网络智能的端到端基于意图的网络。