title: Enhancing Network Management Using Code Generated by Large Language
    Models
summary: 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.
paper_authors._id: 64d9af962668b3a3684d3a9b
paper_authors.name: Sathiya Kumaran Mani
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-17T23:38:15.760Z
paper_authors._id: 64dea237e672e4d2c55d6c07
paper_authors.avatarUrl: /avatars/81d63a4a35f572b49e2292bb6fd5b994.svg
paper_authors.fullname: Sathiya Kumaran Mani
paper_authors.type: user
paper_authors.user: SathiyaMani
paper_authors._id: 64d9af962668b3a3684d3a9c
paper_authors.name: Yajie Zhou
paper_authors._id: 64d9af962668b3a3684d3a9d
paper_authors.name: Kevin Hsieh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-16T13:23:26.989Z
paper_authors._id: 6488e4b0e1b9ce78d3c4a979
paper_authors.avatarUrl: /avatars/d652bc88ec6a7c9c63bffa0c29b9c742.svg
paper_authors.fullname: Kevin Hsieh
paper_authors.type: user
paper_authors.user: kevinhsieh
paper_authors._id: 64d9af962668b3a3684d3a9e
paper_authors.name: Santiago Segarra
paper_authors._id: 64d9af962668b3a3684d3a9f
paper_authors.name: Ranveer Chandra
paper_authors._id: 64d9af962668b3a3684d3aa0
paper_authors.name: Srikanth Kandula
paper_title: Enhancing Network Management Using Code Generated by Large Language
    Models
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) natural-language-based task-specific code natural language queries explainability privacy network operators general program synthesis techniques benchmark applications