LLM-Augmented Knowledge Base Construction For Root Cause Analysis
Summary: arXiv:2604.06171v1 Announce Type: cross
In today’s interconnected world, communications networks serve as the backbone of digital operations, ensuring that users enjoy fast and reliable connectivity. Despite implementing various redundancy and failover mechanisms, achieving “five 9s” (99.999%) reliability remains a formidable challenge. During outages, the need for rapid and accurate root cause analysis (RCA) is paramount to restore services swiftly and mitigate future disruptions.
Study Overview
This study explores three methodologies utilizing Large Language Models (LLMs) to construct a Root Cause Analysis (RCA) Knowledge Base derived from support tickets. The methodologies examined include:
- Fine-Tuning
- Retrieval-Augmented Generation (RAG)
- Hybrid Approach
Each of these methodologies was evaluated based on their performance in generating a comprehensive RCA Knowledge Base. The evaluation utilized a robust set of lexical and semantic similarity metrics to ensure a thorough comparison.
Methodologies Evaluated
- Fine-Tuning: This method involves training LLMs on a specific dataset to enhance their understanding and generate relevant responses based on the nuances of the support tickets.
- Retrieval-Augmented Generation (RAG): This approach combines traditional retrieval techniques with generative models, allowing the system to fetch relevant information from a knowledge base while generating contextually appropriate responses.
- Hybrid Approach: By integrating elements from both the fine-tuning and RAG methodologies, this strategy aims to leverage the strengths of each to produce a more effective RCA Knowledge Base.
Results and Findings
The experiments conducted on a real industrial dataset revealed significant insights into the efficacy of each methodology. The generated knowledge base not only served as a solid foundation for accelerating RCA tasks but also contributed to enhancing overall network resilience.
The study’s findings indicate that the Hybrid Approach outperformed the other two methods, providing a more comprehensive understanding of the root causes of outages. It demonstrated superior performance in terms of both lexical and semantic similarity metrics, thereby affirming the potential of combining different LLM strategies for improved knowledge construction.
Implications for the Future
As organizations continue to rely heavily on robust communications networks, the importance of efficient and accurate RCA processes cannot be overstated. The insights gleaned from this study could pave the way for developing advanced RCA tools that integrate LLM methodologies, ultimately leading to more resilient network infrastructures.
By implementing these innovative approaches, companies can enhance their ability to diagnose issues promptly, leading to reduced downtime and improved customer satisfaction. The advancements in LLM technologies herald a new era for RCA processes, promising to transform how organizations respond to and manage network outages.