Analyzing network topologies and communication graphs plays a crucial role in contemporary network management. However, the absence of a cohesive approach leads to a challenging learning curve, heightened errors, and inefficiencies. In this paper, we introduce a novel approach to facilitate a natural-language-based network management experience, utilizing large language models (LLMs) to generate task-specific code from natural language queries. This method tackles the challenges of explainability, scalability, and privacy by allowing network operators to inspect the generated code, eliminating the need to share network data with LLMs, and concentrating on application-specific requests combined with general program synthesis techniques. We design and evaluate a prototype system using benchmark applications, showcasing high accuracy, cost-effectiveness, and the potential for further enhancements using complementary program synthesis techniques.

翻译：分析网络拓扑和通信图在当代网络管理中扮演着关键角色。然而，缺乏统一的方法导致学习曲线陡峭、错误频发且效率低下。本文提出一种创新方法，通过利用大型语言模型（LLMs）从自然语言查询中生成特定任务的代码，从而促进基于自然语言的网络管理体验。该方法通过允许网络运维人员检查生成的代码、消除与LLMs共享网络数据的必要性，并将应用特定请求与通用程序合成技术相结合，解决了可解释性、可扩展性和隐私性等挑战。我们使用基准应用程序设计并评估了一个原型系统，展现出高精度、成本效益以及通过互补程序合成技术进一步优化的潜力。