Since the advent of GPT, large language models (LLMs) have brought about revolutionary advancements in all walks of life. As a superior natural language processing (NLP) technology, LLMs have consistently achieved state-of-the-art performance on numerous areas. However, LLMs are considered to be general-purpose models for NLP tasks, which may encounter challenges when applied to complex tasks in specialized fields such as optical networks. In this study, we propose a framework of LLM-empowered optical networks, facilitating intelligent control of the physical layer and efficient interaction with the application layer through an LLM-driven agent (AI-Agent) deployed in the control layer. The AI-Agent can leverage external tools and extract domain knowledge from a comprehensive resource library specifically established for optical networks. This is achieved through user input and well-crafted prompts, enabling the generation of control instructions and result representations for autonomous operation and maintenance in optical networks. To improve LLM's capability in professional fields and stimulate its potential on complex tasks, the details of performing prompt engineering, establishing domain knowledge library, and implementing complex tasks are illustrated in this study. Moreover, the proposed framework is verified on two typical tasks: network alarm analysis and network performance optimization. The good response accuracies and sematic similarities of 2,400 test situations exhibit the great potential of LLM in optical networks.

翻译：自GPT问世以来，大语言模型（LLMs）已在各行各业带来革命性进展。作为一种卓越的自然语言处理（NLP）技术，LLMs在众多领域持续取得最先进的性能表现。然而，LLMs被视为面向NLP任务的通用模型，在应用于光网络等专业领域的复杂任务时可能面临挑战。本研究提出一种LLM赋能的光网络框架，通过在控制层部署LLM驱动的智能体（AI-Agent），实现对物理层的智能控制以及与应用层的高效交互。该AI-Agent能够借助外部工具，并从专门为光网络构建的综合资源库中提取领域知识。通过用户输入和精心设计的提示指令，该框架可生成用于光网络自主运维的控制指令与结果表征。为提升LLM在专业领域的能力并激发其处理复杂任务的潜力，本研究详细阐述了提示工程实施、领域知识库构建及复杂任务执行的具体方案。此外，所提框架在两个典型任务上得到验证：网络告警分析与网络性能优化。在2400个测试场景中取得的良好响应准确率与语义相似度，充分展现了LLM在光网络领域的巨大潜力。