mlsanityData to 14 Oct 2026

A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

arXiv 2412.19823 ↗First posted 16 Dec 2024cs.NI cs.AI cs.LGcited by 25

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Abstract (arXiv)

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 91 of its 188 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

91 of 188 references.

References · 91

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50 most cited of 91 All 91 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 17; 2026: 8.

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  1. 2025A Survey on Collaborative Mechanisms Between Large and Small Language Modelscited by 14
  2. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 14
  3. 2025Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulationcited by 13
  4. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  5. 2025MX-AI: Agentic Observability and Control Platform for Open and AI-RANcited by 4
  6. 2026SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshootingcited by 4
  7. 2025Integrating Language Models for Enhanced Network State Monitoring in DRL-Based SFC Provisioningcited by 2
  8. 2025From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communicationscited by 2
  9. 2025LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioningcited by 2
  10. 2025Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systemscited by 2
  11. 2026EDCO: Dynamic Curriculum Orchestration for Domain-specific Large Language Model Fine-tuningcited by 2
  12. 2025Distilling Large Language Models for Network Active Queue Managementcited by 1
13 more
  1. 2025LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modelingcited by 1
  2. 2025Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Thingscited by 1
  3. 2026Performance Comparison of IBN orchestration using LLM and SLMscited by 1
  4. 2026MetaKube: An Experience-Aware LLM Framework for Kubernetes Failure Diagnosiscited by 1
  5. 2026A Reproducible Semantic Benchmark for Multivendor DSM-to-CLI Translationcited by 1
  6. 2025Robust Transmission of Punctured Text with Large Language Model-based Recoverycited by 0
  7. 2025Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Servicescited by 0
  8. 2025The Future of Internet of Things and Multimodal Language Models in 6G Networks: Opportunities and Challengescited by 0
  9. 2025Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challengescited by 0
  10. 2025ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysiscited by 0
  11. 2026Customized User Plane Processing via Code Generating AI Agents for Next Generation Mobile Networkscited by 0
  12. 2026Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensemblingcited by 0
  13. 2026Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networkscited by 0