Leverage large language model (LLM) to refer the fault is considered to be a potential solution for intelligent network fault diagnosis. However, how to represent network information in a paradigm that can be understood by LLMs has always been a core issue that has puzzled scholars in the field of network intelligence. To address this issue, we propose LLM-based Network Semantic Generation (LNSG) algorithm, which integrates semanticization and symbolization methods to uniformly describe the entire multi-modal network information. Based on the LNSG and LLMs, we present NetSemantic, a plug-and-play, data-independent, network information semantic fault diagnosis framework. It enables rapid adaptation to various network environments and provides efficient fault diagnosis capabilities. Experimental results demonstrate that NetSemantic excels in network fault diagnosis across various complex scenarios in a zero-shot manner.

翻译：利用大语言模型（LLM）进行故障定位被认为是智能网络故障诊断的一种潜在解决方案。然而，如何以LLM能够理解的范式来表示网络信息，一直是困扰网络智能化领域学者的核心问题。为解决此问题，我们提出了基于LLM的网络语义生成（LNSG）算法，该算法融合语义化与符号化方法，以统一描述整个多模态网络信息。基于LNSG与LLM，我们提出了NetSemantic——一个即插即用、数据无关的网络信息语义故障诊断框架。它能够快速适配多种网络环境，并提供高效的故障诊断能力。实验结果表明，NetSemantic能够在零样本方式下，在各种复杂场景的网络故障诊断中表现出色。