Enhancing Network Management Using Code Generated by Large Language Models
This addresses inefficiencies and errors in network management for operators, though it appears incremental as it builds on existing LLM and program synthesis techniques.
The paper tackles the problem of network management by introducing a natural-language-based approach that uses large language models (LLMs) to generate task-specific code from queries, resulting in high accuracy and cost-effectiveness.
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.