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.

翻译：大语言模型正在人工智能应用中迅速崛起，尤其在自然语言处理与生成式AI领域。这类模型不仅限于文本生成任务，本身还具备利用提示工程的机会——即通过适当结构化模型输入，明确阐述模型目标。意图驱动网络便是典型范例，这种新兴方法旨在实现网络运营与管理的自动化维护。本文提出语义路由机制，以提升大语言模型辅助的5G核心网络意图驱动管理与编排性能。研究构建了端到端意图提取框架，建立了包含多样化用户意图样本的数据集，并深入分析了编码器与量化对系统整体性能的影响。结果表明，相较于采用提示架构的独立大语言模型，引入语义路由器可有效提升大语言模型部署的准确率与效率。