A benchmark finds prompt-based LLMs achieve F1 scores of 0.82-0.91 for log anomaly detection in zero-shot settings without any labeled training data, while fine-tuned transformers reach 0.96-0.99.
DeBERTa: Decoding-enhanced BERT with disentangled attention,
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LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics
A benchmark finds prompt-based LLMs achieve F1 scores of 0.82-0.91 for log anomaly detection in zero-shot settings without any labeled training data, while fine-tuned transformers reach 0.96-0.99.