Large language models (LLMs) are rapidly emerging in Artificial Intelligence
(AI) applications, especially in the fields of natural language processing and
generative AI. Not limited to text generation applications, these models
inherently possess the opportunity to leverage prompt engineering, where the
inputs of such models can be appropriately structured to articulate a model's
purpose explicitly. A prominent example of this is intent-based networking, an
emerging approach for automating and maintaining network operations and
management. This paper presents semantic routing to achieve enhanced
performance in LLM-assisted intent-based management and orchestration of 5G
core networks. This work establishes an end-to-end intent extraction framework
and presents a diverse dataset of sample user intents accompanied by a thorough
analysis of the effects of encoders and quantization on overall system
performance. The results show that using a semantic router improves the
accuracy and efficiency of the LLM deployment compared to stand-alone LLMs with
prompting architectures.

这篇论文介绍了语义路由，以实现在 LLM 辅助下的 5G 核心网络的基于意图的管理和编排，通过建立一个端到端的意图提取框架，并提供一个多样化的样本用户意图数据集以及对编码器和量化对系统整体性能的影响的全面分析，结果显示使用语义路由器相较于独立的 LLMs 与提示架构相比，能提高 LLM 部署的准确性和效率。

LLM 辅助意图驱动的 5G 核心网络管理和编排的语义路由增强性能

Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based  5G Core Network Management and Orchestration

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.

这篇论文讲述了将机器学习和人工智能整合到第五代（5G）网络中，揭示了网络智能的局限性，以及当前和下一代设备对网络智能的越来越苛刻的要求。本文提出了为 5G 和下一代基于意图的网络开发定制的大型语言模型（LLM），并提供了对未来 LLM 发展和集成的见解，以实现全自动网络智能的端到端意图型网络。

朝着基于意图的网络管理：5G 核心网络中的意图提取的大型语言模型

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

Intent-Based Networking (IBN) presents a paradigm shift for network
management, by promising to align intents and business objectives with network
operations--in an automated manner. However, its practical realization is
challenging: 1) processing intents, i.e., translate, decompose and identify the
logic to fulfill the intent, and 2) intent conformance, that is, considering
dynamic networks, the logic should be adequately adapted to assure intents. To
address the latter, intent assurance is tasked with continuous verification and
validation, including taking the necessary actions to align the operational and
target states. In this paper, we define an assurance framework that allows us
to detect and act when intent drift occurs. To do so, we leverage AI-driven
policies, generated by Large Language Models (LLMs) which can quickly learn the
necessary in-context requirements, and assist with the fulfillment and
assurance of intents.

基于意图的网络管理通过使用人工智能驱动的策略来满足和保证意图，帮助实现网络管理与业务目标的自动化对齐。

使用意图漂移引导的 LLMs 进行意图保证

Intent Assurance using LLMs guided by Intent Drift

The computer network world is changing and the NetDevOps approach has brought
the dynamics of applications and systems into the field of communication
infrastructure. Businesses are changing and businesses are faced with
difficulties related to the diversity of hardware and software that make up
those infrastructures. The "Intent-Based Networking - Concepts and Definitions"
document describes the different parts of the ecosystem that could be involved
in NetDevOps. The recognize, generate intent, translate and refine features
need a new way to implement algorithms. This is where artificial intelligence
comes in.

本文介绍了 NetDevOps 及意图为基础网络的概念，探讨了应用人工智能实现新算法的必要性。