Główne pojęcia
Large language models can be leveraged for effective log parsing through in-context learning, as demonstrated by DivLog.
Streszczenie
DivLog proposes a log parsing framework based on large language models (LLMs) and in-context learning (ICL). It samples diverse logs offline, selects appropriate examples for each target log during parsing, and generates log templates without model tuning. DivLog achieves state-of-the-art performance with high accuracy metrics across 16 datasets. The framework enhances the quality of generated log templates and demonstrates stability and robustness in log analysis tasks.
Statystyki
DivLog achieves 98.1% Parsing Accuracy, 92.1% Precision Template Accuracy, and 92.9% Recall Template Accuracy on average.
LogPPT extracts virtual labels from adaptively selected logs for model training.
Drain assumes leading tokens are constants but faces limitations with flexible log structures.
Cytaty
"DivLog samples diverse logs offline and selects appropriate examples for each target log during parsing."
"DivLog achieves state-of-the-art performance with high accuracy metrics across 16 datasets."
"In-context learning enables LLMs to generate accurate log templates without model tuning."