Leveraging Large Language Models For System Log Analysis - Fault Troubleshooting Radio Units Using Log Data
| dc.contributor.author | Nir, Jacob | |
| dc.contributor.author | Snäll, William | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering | en |
| dc.contributor.examiner | Damaschke, Peter | |
| dc.contributor.supervisor | Haghir Chehreghani,, Morteza | |
| dc.date.accessioned | 2025-01-03T13:35:19Z | |
| dc.date.available | 2025-01-03T13:35:19Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | ||
| dc.description.abstract | 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. | |
| dc.identifier.coursecode | DATX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/309048 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | AI | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Large Language Models | |
| dc.subject | Generative AI | |
| dc.subject | System Logs | |
| dc.title | Leveraging Large Language Models For System Log Analysis - Fault Troubleshooting Radio Units Using Log Data | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Engineering mathematics and computational science (MPENM), MSc | |
| local.programme | Complex adaptive systems (MPCAS), MSc |