Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks
<|reference_start|>Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks: 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.<|reference_end|>
@article{manias2024towards,
    title={Towards Intent-Based Network Management: Large Language Models for
  Intent Extraction in 5G Core Networks},
    author={Dimitrios Michael Manias and Ali Chouman and Abdallah Shami},
    journal={arXiv preprint arXiv:2403.02238},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02238},
    primaryClass={cs.NI cs.AI}
}