用大模型构建故障分析知识库，提升网络恢复效率
LLM-Augmented Knowledge Base Construction For Root Cause Analysis
- 用LLM从工单中自动构建故障知识库，三种方法对比
- 混合方法在语义相似度上表现最优，显著加速故障定位
- 适合网络运维与AI结合的工程师快速上手
通信网络是数字世界的核心，尽管具备冗余和容灾机制，仍难以保证99.999%的可靠性，因此故障发生时需快速准确地进行根因分析（RCA）。本文评估了三种大语言模型（LLM）方法——微调、检索增强生成（RAG）及混合方法——在从支持工单中构建RCA知识库上的表现。通过一系列词法与语义相似度指标进行比较，实验基于真实工业数据集，结果表明生成的知识库可作为加速RCA任务的优质起点，有效提升网络韧性。
原文摘要 · Abstract (English)
Communications networks now form the backbone of our digital world, with fast and reliable connectivity. However, even with appropriate redundancy and failover mechanisms, it is difficult to guarantee "five 9s" (99.999 %) reliability, requiring rapid and accurate root cause analysis (RCA) during outages. In the event of an outage, rapid and accurate RCA becomes essential to restore service and prevent future disruptions. This study evaluates three Large Language Model (LLM) methodologies - Fine-Tuning, RAG, and a Hybrid approach - for constructing a Root Cause Analysis (RCA) Knowledge Base from support tickets. We compare their performance using a comprehensive suite of lexical and semantic similarity metrics. Our experiments on a real industrial dataset demonstrate that the generated knowledge base provides an excellent starting point for accelerating RCA tasks and improving network resilience.
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