Large Language Models (LLMs) for Log Parsing and Documentation
The paper explores the implementation of LLMs for parsing system logs and producing technical summaries and the advantages compared to conventional rule-based methods are explored.
Abstract
Large language models (LLMs) represent an advanced artificial intelligence approach with considerable potential for natural language processing tasks. Their capacity for comprehending and synthesizing human-like text makes them well-suited for applications including log file analysis and automated documentation generation. The paper explores the implementation of LLMs for parsing system logs and producing technical summaries. The advantages compared to conventional rule-based methods, recommended implementation strategies, challenges, and directions for further research are examined.