# Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks

arXiv: https://arxiv.org/abs/2403.02238 · HTML: https://mlsanity.com/paper/2403.02238/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis](https://mlsanity.com/paper/2205.15121/) (2022)
- [An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization](https://mlsanity.com/paper/2209.10428/) (2022)

## Built on by (most cited first, 17 of 17)

- [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)
- [Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions](https://mlsanity.com/paper/2407.04581/) (2024)
- [Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration](https://mlsanity.com/paper/2404.15869/) (2024)
- [LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs](https://mlsanity.com/paper/2412.19811/) (2024)
- [Towards End-to-End Network Intent Management with Large Language Models](https://mlsanity.com/paper/2504.13589/) (2025)
- [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025)
- [Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model](https://mlsanity.com/paper/2508.06616/) (2025)
- [Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things](https://mlsanity.com/paper/2512.20627/) (2025)
- [Vision Language Models for Optimization-Driven Intent Processing in Autonomous Networks](https://mlsanity.com/paper/2601.12744/) (2026)
- [IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networks](https://mlsanity.com/paper/2601.13114/) (2026)
- [Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes](https://mlsanity.com/paper/2608.22644/) (2026)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems](https://mlsanity.com/paper/2606.24416/) (2026)
- [Engineering Trustworthy Agentic AI for Critical Systems](https://mlsanity.com/paper/2607.18548/) (2026)
- [Benchmarking LLM-Guided Control-Plane Policies for Backend Fault Isolation in HAProxy](https://mlsanity.com/paper/2608.10532/) (2026)
- [From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents](https://mlsanity.com/paper/2610.09039/) (2026)
- [When LLM Agents See a Conflict but Still Proceed: Cross-Domain Dependencies in Network Slicing](https://mlsanity.com/paper/2610.17118/) (2026)

## Abstract (arXiv)

The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices. This transition to ubiquitous intelligence demands high connectivity, synchronicity, and end-to-end communication between users and network operators, and will pave the way towards full network automation without human intervention. Intent-based networking is a key factor in the reduction of human actions, roles, and responsibilities while shifting towards novel extraction and interpretation of automated network management. This paper presents the development of a custom Large Language Model (LLM) for 5G and next-generation intent-based networking and provides insights into future LLM developments and integrations to realize end-to-end intent-based networking for fully automated network intelligence.