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LLM-Augmented Knowledge Base Construction For Root Cause Analysis

arXiv cs.CL / 4/9/2026

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Key Points

  • The paper proposes building an RCA knowledge base from support tickets to speed up and improve root cause analysis during communications network outages when “five 9s” reliability can’t be guaranteed.
  • It evaluates three LLM-based knowledge base construction methods—fine-tuning, retrieval-augmented generation (RAG), and a hybrid approach—using both lexical and semantic similarity metrics.
  • Experiments on a real industrial dataset show the resulting knowledge base is effective as an initial resource for accelerating RCA and supporting higher network resilience.
  • The study focuses on performance comparison methodology and shows practical potential for turning unstructured ticket data into an actionable RCA asset.
  • Overall, the work targets operational continuity by reducing outage diagnosis time and helping prevent repeat disruptions through better RCA outputs.

Abstract

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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LLM-Augmented Knowledge Base Construction For Root Cause Analysis | AI Navigate