In a modern networking environment, analyzing network structures is pivotal. However, existing approaches lack cohesiveness, leading to a steep learning curve and heightened errors. This paper introduces a groundbreaking method where Large Language Models (LLMs) are employed to generate task-specific code from natural language inquiries, streamlining network management. This allows for a more natural, language-based interaction, while addressing concerns related to explainability, scalability, and privacy. By facilitating code inspection and eliminating the need to transfer sensitive data to LLMs, this method offers a more efficient, accurate, and cost-effective approach to network management.
Publication date: 11 Aug 2023
Project Page: ?
Paper: https://arxiv.org/pdf/2308.06261.pdf
