mlsanityData to 7 Oct 2026

LLM-Augmented Knowledge Base Construction For Root Cause Analysis

arXiv 2604.06171 ↗First posted 9 Jan 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 45 references that are in the map. First posted on arXiv 9 Jan 2026.

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References · 20

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8 more
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  2. 2026Stalled, Biased, and Confused: Uncovering Reasoning Failures in LLMs for Cloud-Based Root Cause Analysis
  3. 2025Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks
  4. 2024Exploring LLM-based Agents for Root Cause Analysis
  5. 2025The Multi-Agent Fault Localization System Based on Monte Carlo Tree Search Approach
  6. 2026How Far Can Root Cause Analysis Go on Real-World Telemetry Data?
  7. 2026LATS-RCA: Language Agent Tree Search for Root Cause Analysis in Microservices
  8. 2026Root Cause Analysis Method Based on Large Language Models with Residual Connection Structures