# Large Language Models for Networking: Applications, Enabling Techniques, and Challenges

arXiv: https://arxiv.org/abs/2311.17474 · HTML: https://mlsanity.com/paper/2311.17474/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2023.

## Built on by (most cited first, 28 of 28)

- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems](https://mlsanity.com/paper/2402.01748/) (2024)
- [Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends](https://mlsanity.com/paper/2409.14457/) (2024)
- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
- [Large Language Models for Networking: Workflow, Advances and Challenges](https://mlsanity.com/paper/2404.12901/) (2024)
- [Using Large Language Models to Understand Telecom Standards](https://mlsanity.com/paper/2404.02929/) (2024)
- [Linguistic Intelligence in Large Language Models for Telecommunications](https://mlsanity.com/paper/2402.15818/) (2024)
- [Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach](https://mlsanity.com/paper/2407.00978/) (2024)
- [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025)
- [NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration](https://mlsanity.com/paper/2604.09678/) (2026)
- [On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks](https://mlsanity.com/paper/2409.15246/) (2024)
- [Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense](https://mlsanity.com/paper/2412.21051/) (2024)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting](https://mlsanity.com/paper/2605.04530/) (2026)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA](https://mlsanity.com/paper/2602.20492/) (2026)
- [Large Language Model-Driven Cross-Domain Orchestration Using Multi-Agent Workflow](https://mlsanity.com/paper/2410.10831/) (2024)
- [LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling](https://mlsanity.com/paper/2505.04101/) (2025)
- [TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning](https://mlsanity.com/paper/2505.13831/) (2025)
- [Performance Comparison of IBN orchestration using LLM and SLMs](https://mlsanity.com/paper/2603.06647/) (2026)
- [Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations](https://mlsanity.com/paper/2604.07264/) (2026)
- [TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization](https://mlsanity.com/paper/2509.00914/) (2025)
- [LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Cost](https://mlsanity.com/paper/2511.07865/) (2025)
- [Grounding Large Language Models as Generalizable Policies in Network Control](https://mlsanity.com/paper/2512.11839/) (2025)
- [Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning](https://mlsanity.com/paper/2606.22878/) (2026)
- [A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation](https://mlsanity.com/paper/2607.22400/) (2026)
- [Safety Signals to Verify NetOps Agents with Action-Level Granularity](https://mlsanity.com/paper/2609.14422/) (2026)

## Abstract (arXiv)

The rapid evolution of network technologies and the growing complexity of network tasks necessitate a paradigm shift in how networks are designed, configured, and managed. With a wealth of knowledge and expertise, large language models (LLMs) are one of the most promising candidates. This paper aims to pave the way for constructing domain-adapted LLMs for networking. Firstly, we present potential LLM applications for vertical network fields and showcase the mapping from natural language to network language. Then, several enabling technologies are investigated, including parameter-efficient finetuning and prompt engineering. The insight is that language understanding and tool usage are both required for network LLMs. Driven by the idea of embodied intelligence, we propose the ChatNet, a domain-adapted network LLM framework with access to various external network tools. ChatNet can reduce the time required for burdensome network planning tasks significantly, leading to a substantial improvement in efficiency. Finally, key challenges and future research directions are highlighted.