Imagine having an AI assistant that can automatically detect unusual patterns in your data, like a sudden spike in website traffic or a dip in sales. That's the promise of anomaly detection in time series data – sequences of data points collected over time. Researchers are exploring whether large language models (LLMs), known for their text processing prowess, can also be effective anomaly detectors. A new study introduces SIGLLM, a framework that converts time series data into a text-like format that LLMs can understand. It uses two main approaches: First, it directly asks the LLM to identify anomalies in the data, like asking, "What looks out of place here?" Second, it leverages the LLM's ability to predict future data points. By comparing the LLM's predictions to the actual data, it can highlight discrepancies that suggest anomalies. The results are promising. While deep learning models still outperform LLMs in anomaly detection, the study shows that LLMs can indeed identify unusual patterns, sometimes even rivaling traditional statistical methods. This research opens exciting possibilities for using LLMs in new ways, potentially simplifying anomaly detection and making it accessible to a wider audience. However, challenges remain, such as the LLMs' limited ability to handle long sequences of data and the computational costs involved. As LLMs continue to evolve, they could become valuable tools for automatically spotting those critical anomalies that might otherwise go unnoticed.