用自然语言控制5G网络，智能识别意图并自动配置。
Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes
- 通过语义路由器解析操作员的自然语言指令。
- 动态路由在真实提示下准确提取配置细节，成功率高。
- 适合网络自动化、智能运维方向的研究者与工程师。
随着人工智能和大语言模型（LLMs）在日常应用中的普及，其理解自然语言的能力显著提升。将AI融入网络管理与编排成为新趋势，其中一种典型应用是基于意图的网络管理，即网络管理员通过自然语言控制网络。本文提出在基于意图的5G+核心网中，采用动态路由与语义路由器，从管理员的输入中识别意图，并提取实现该意图所需的关键信息。同时，通过评估多种编码器及动态路由在真实操作提示下的细节提取准确率，验证了静态与动态路由在信息提取和模式格式化方面的有效性。实验结果表明，两种方法均能高效完成意图解析任务。
原文摘要 · Abstract (English)
As the use of Artificial Intelligence (AI) and Large Language Models (LLMs) is becoming common in everyday applications, their ability to interpret natural language has increased significantly. An emerging application of AI is integration with network management and orchestration practices. An instance of this integration is LLM-enhanced intent-based networking, where network operators will control a network using natural language. This work presents the use of dynamic routes with a semantic router to identify an intent from a network operator's prompt and extract necessary details for intent fulfillment in intent-based 5G+ core networks. Furthermore, the performance of static route selection is assessed by evaluating multiple encoders and dynamic route detail extraction accuracy against a series of realistic operator prompts. The presented results show that static and dynamic routes are successful in detail extraction and schema formatting.
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