mlsanityData to 7 Oct 2026

Enhancing Network Management Using Code Generated by Large Language Models

arXiv 2308.06261 ↗First posted 11 Aug 2023cs.NI cs.AIcited by 20

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Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 26 of its 62 references that are in the map. First posted on arXiv 11 Aug 2023.

Builds on

26 of 62 references.

References · 26

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 8; 2025: 6; 2026: 4.

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  1. 2023CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Servingcited by 199
  2. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  3. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 70
  4. 2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systemscited by 19
  5. 2024Large Language Models for Networking: Workflow, Advances and Challengescited by 15
  6. 2024Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigmcited by 12
  7. 2024Can LLMs Understand Computer Networks? Towards a Virtual System Administratorcited by 11
  8. 2025NetArena: Dynamic Benchmarks for AI Agents in Network Automationcited by 8
  9. 2024Large Language Models meet Network Slicing Management and Orchestrationcited by 7
  10. 2024Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarioscited by 6
  11. 2025MoE2: Optimizing Collaborative Inference for Edge Large Language Modelscited by 6
  12. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
8 more
  1. 2025An LLM-based Agentic Framework for Accessible Network Controlcited by 2
  2. 2025From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communicationscited by 1
  3. 2024Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networkscited by 0
  4. 2025Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Thingscited by 0
  5. 2026Toward Non-Expert Customized Congestion Control: Large Language Model-Assisted CCA Code Generation with eBPF Deploymentcited by 0
  6. 2026NetConfArena: An Executable Benchmark for LLM Agents in Closed-Loop Network Configurationcited by 0
  7. 2026Can AI Agents Deliver Verifiable Network-Wide Outcomes Across Authority Boundaries?cited by 0
  8. 2026Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systemscited by 0