LLMs for Log Analysis: A Review

The rapid evolution of information and communication systems has resulted in the generation of massive amounts of log data, presenting unique opportunities for system monitoring and management. This review paper explores recent advancements in log analysis, with a particular focus on anomaly detection and system management techniques. The literature highlights a shift from traditional statistical approaches toward semantically-aware methods, driven largely by the integration of Large Language Models (LLMs). While log parsing remains essential for structuring raw log data, its inability to capture complex semantic patterns has led researchers to investigate alternative methods such as clustering, frequent ngram mining, and one-to-one mapping. The unsupervised and semi-supervised techniques, along with deep learning methods have gained prominence for their ability to analyze sequential log data and detect anomalies. Additional models like MultiLog, which leverage pretrained language models such as BERT, have shown promise in minimizing the need for labelled data. Although still in its early stages, the integration of LLMs into log-based forecasting and anomaly detection presents considerable potential. This review concludes by emphasizing the need for future research to refine LLM applications and develop robust semantic analysis techniques to tackle the growing complexity of modern log data.