Leveraging Large Language Models For System Log Analysis - Fault Troubleshooting Radio Units Using Log Data
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Complex adaptive systems (MPCAS), MSc
Complex adaptive systems (MPCAS), MSc
Publicerad
2024
Författare
Nir, Jacob
Snäll, William
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis investigates the application of large language models (LLMs) for system
log analysis, specifically focusing on fault troubleshooting in radio units using
log data. The primary objective is to enhance the efficiency and accuracy of system
monitoring tools through state-of-the-art AI techniques. The research explores
the utilization of retrieval-augmented generation (RAG) frameworks and parameterefficient
fine-tuning (PEFT) methods to process and summarize log data. By employing
pre-trained models such as Llama2, Llama3 and Mistral, the study evaluates
different implementations to summarize segments of logs as well as extracting relevant
information from them.
The findings demonstrate that LLMs can significantly automate and improve the
analysis of system logs, providing insights and facilitating easier troubleshooting.
Additionally, the study examines the impact of enriching chatbot input data with
contextual information, leading to substantial performance improvements in specialized
domains. Despite the promising results, the research acknowledges limitations
related to the quality and structure of log data and the need for source-specific
refinements in context-enrichment methods.
The contributions of this thesis are twofold: it presents a viable approach to leveraging
LLMs for easier system monitoring and highlights the critical role of context
in enhancing chatbot functionalities. Future research directions include integrating
more advanced models, fine tuning existing models and exploring other state-of-theart
methods to optimize retrieval-augmented generation pipelines.
Beskrivning
Ämne/nyckelord
AI , Artificial Intelligence , Large Language Models , Generative AI , System Logs