Abstract
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.
Abstract (translated)
大语言模型（LLMs）在人工智能（AI）应用中正在迅速崛起，尤其是在自然语言处理和生成式人工智能领域。这些模型不仅限于文本生成应用，还具有利用提示工程的机会，使模型的输入适当地结构化以明确表达其目的。一个显著的例子是基于意图的网络，这是一种自动化和维护网络操作和管理的新兴方法。本文介绍了语义路由以实现LLM辅助意图基于管理的增强性能和5G核心网络的编排。这项工作建立了一个端到端的意图提取框架，并提供了丰富的用户意图数据集以及编码器和量化对整体系统性能的影响的深入分析。结果表明，使用语义路由器可以提高LLM部署的准确性和效率，与单独使用提示结构的LLM相比。
URL
https://arxiv.org/abs/2404.15869
