Papers
arxiv:2308.06261

Enhancing Network Management Using Code Generated by Large Language Models

Published on Aug 11, 2023
· Submitted by akhaliq on Aug 14, 2023
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Abstract

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.

Community

Hi, very interesting paper. I went looking for your open source source code as mentioned in the paper: https://github.com/microsoft/NeMoEval I couldn't find it. Has it been released yet?

Paper author

Thank you for expressing interest in our paper! We are currently putting the finishing touches on the code and plan to make it publicly available by next week.

@kevinhsieh Awesome, thank you for responding so promptly

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