利用大型语言模型生成的代码增强网络管理

摘要

分析网络拓扑和通信图在当代网络管理中起着至关重要的作用。然而，缺乏一种统一的方法会导致学习曲线陡峭，错误率增加，效率降低。本文介绍了一种新方法，旨在促进基于自然语言的网络管理体验，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。该方法通过允许网络操作员检查生成的代码来解决可解释性、可扩展性和隐私性方面的挑战，消除了与LLMs共享网络数据的需求，并集中于结合通用程序合成技术的应用特定请求。我们设计并评估了一个原型系统，使用基准应用展示了高准确性、成本效益以及利用互补程序合成技术进一步增强的潜力。

English

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.

利用大型语言模型生成的代码增强网络管理

Enhancing Network Management Using Code Generated by Large Language Models

摘要

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