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Found 6 papers, 2 papers with code
Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics.
MLAD: A Unified Model for Multi-system Log Anomaly Detection
In spite of the rapid advancements in unsupervised log anomaly detection techniques, the current mainstream models still necessitate specific training for individual system datasets, resulting in costly procedures and limited scalability due to dataset size, thereby leading to performance bottlenecks.
LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection
Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps).
OWL: A Large Language Model for IT Operations
However, there is a lack of specialized LLMs for IT operations.
LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction
Experiments on five benchmarks validate the effectiveness of LogLG for detecting anomalies on unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly supervised method, achieves significant performance improvements compared to existing methods.
TransLog: A Unified Transformer-based Framework for Log Anomaly Detection
Therefore, we propose a unified Transformer-based framework for log anomaly detection (\ourmethod{}), which is comprised of the pretraining and adapter-based tuning stage.
