Revolutionizing Optical Networks: The Integration and Impact of Large Language Models

Large Language Models (LLMs), based on the transformer architecture, are emerging as powerful enablers of automation in optical network management. Optical networks present unique challenges—such as real-time performance requirements, multi-vendor interoperability, and physical-layer impairments including chromatic dispersion, polarization mode dispersion, and nonlinear effects—which demand intelligent, adaptable control strategies. Traditional automation approaches struggle with these complexities due to their dependence on rigid rule sets and narrow-task machine learning models. LLMs offer a paradigm shift by enabling context-aware reasoning, seamless multi-vendor integration, and dynamic task generalization, making them especially well-suited to the demands of optical networks. LLMs can automate a wide range of network management tasks, such as configuration, fault diagnosis, alarm correlation, and routing and spectrum assignment (RSA). Their ability to interpret heterogeneous data and understand domain-specific language enhances Quality of Transmission (QoT) estimation, amplifier gain optimization, and scenario modeling. Additionally, LLMs support intuitive human–machine interaction and Human-in-the-Loop (HITL) decision-making, improving transparency and operational control. This paper introduces a unified framework that integrates LLMs with Digital Twin (DT) technology to enable real-time monitoring, predictive analytics, and scenario-based optimization in virtualized environments. The synergy between LLMs and DTs reduces operational complexity, improves resource efficiency, and supports autonomous decision-making. However, LLMs face limitations including hallucinations—plausible but incorrect outputs—and computational latency, especially in time-sensitive contexts such as dynamic reconfiguration and fault recovery. To mitigate these issues, we explore prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning. Strategies such as edge computing and model pruning reduce latency and improve resource utilization. Energy efficiency is enhanced through GPU scaling, model parallelism, algorithmic refinement, and renewable energy integration. With appropriate adaptation, LLMs can substantially enhance automation, scalability, and sustainability in optical networks. By consolidating diverse functions into a unified intelligent system, LLMs enable greener, more resilient, and self-optimizing next-generation optical infrastructures.