Boosting Your Anomaly Detection With LLMs

Large Language Models (LLMs) can enhance anomaly detection through several emerging application patterns. One direct approach uses an LLM to analyze data, such as system logs or time-series data converted to text, and identify anomalies based on its pre-trained knowledge. LLMs can also serve as powerful tools for data augmentation by generating synthetic anomalous samples to balance datasets for supervised learning. Other methods include using LLMs to provide natural language explanations for detected anomalies and creating better feature representations (embeddings) for traditional detection algorithms.