Core Concepts
DivLog proposes a log parsing framework based on in-context learning, achieving state-of-the-art performance in accuracy metrics across various datasets.
Abstract
DivLog introduces a novel approach to log parsing by leveraging large language models and in-context learning. The framework demonstrates exceptional accuracy and robustness compared to existing log parsers. By sampling diverse logs and selecting appropriate examples for prompting, DivLog achieves high parsing accuracy, precision template accuracy, and recall template accuracy on multiple datasets.
Stats
DivLog achieves an average Parsing Accuracy of 98.1%.
The Precision Template Accuracy of DivLog is 92.1%.
DivLog attains a Recall Template Accuracy of 92.9%.
Quotes
"DivLog samples a small amount of offline logs as candidates by maximizing their diversity."
"DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt."
"DivLog generates log templates without necessitating model tuning."