Towards End-to-End Network Intent Management with Large Language Models
By: Lam Dinh , Sihem Cherrared , Xiaofeng Huang and more
Potential Business Impact:
Computers build phone networks from simple instructions.
Large Language Models (LLMs) are likely to play a key role in Intent-Based Networking (IBN) as they show remarkable performance in interpreting human language as well as code generation, enabling the translation of high-level intents expressed by humans into low-level network configurations. In this paper, we leverage closed-source language models (i.e., Google Gemini 1.5 pro, ChatGPT-4) and open-source models (i.e., LLama, Mistral) to investigate their capacity to generate E2E network configurations for radio access networks (RANs) and core networks in 5G/6G mobile networks. We introduce a novel performance metrics, known as FEACI, to quantitatively assess the format (F), explainability (E), accuracy (A), cost (C), and inference time (I) of the generated answer; existing general metrics are unable to capture these features. The results of our study demonstrate that open-source models can achieve comparable or even superior translation performance compared with the closed-source models requiring costly hardware setup and not accessible to all users.
Similar Papers
Intent-Based Network for RAN Management with Large Language Models
Networking and Internet Architecture
Makes wireless networks smarter and more energy-efficient.
Intent-Based Network for RAN Management with Large Language Models
Networking and Internet Architecture
Makes cell towers smarter to save energy.
NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation
Networking and Internet Architecture
Computers understand network needs from simple words.