利用大型語言模型生成的程式碼增強網路管理

摘要

分析網絡拓撲和通信圖在當代網絡管理中扮演著至關重要的角色。然而，缺乏一致性方法導致學習曲線陡峭，錯誤率增加，效率降低。本文介紹了一種新方法，以促進基於自然語言的網絡管理體驗，利用大型語言模型（LLMs）從自然語言查詢中生成特定任務代碼。該方法應對了可解釋性、可擴展性和隱私性方面的挑戰，讓網絡運營商可以檢查生成的代碼，消除了與LLMs共享網絡數據的需求，並集中於結合應用程序特定請求和通用程序合成技術。我們設計並評估了一個原型系統，使用基準應用程序展示了高準確性、成本效益以及使用互補程序合成技術進行進一步增強的潛力。

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.

利用大型語言模型生成的程式碼增強網路管理

Enhancing Network Management Using Code Generated by Large Language Models

摘要

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