Anomaly detection (AD) is like finding a needle in a haystack. It's crucial for spotting fraud, diagnosing illnesses, and even filtering spam. But what if we could use the power of large language models (LLMs) to make this process easier? New research explores exactly that. Researchers have created a benchmark called AD-LLM to test how LLMs can help with NLP anomaly detection. They looked at three key areas: zero-shot detection (using the LLM's existing knowledge without specific training), data augmentation (creating synthetic data to improve existing AD models), and model selection (having the LLM choose the best AD model for a given dataset). The results are promising. LLMs excelled at zero-shot detection, often outperforming traditional methods. Providing extra context, like the name of the anomaly category, boosted performance even further. While data augmentation proved useful, its effectiveness varied depending on the complexity of the AD model and the dataset itself. Model selection presented a unique challenge. While LLMs could pick models that performed well, their reasoning often lacked specific insights tied to the dataset. They might say a model was "good for high-dimensional data" without explaining why that was relevant. This research shows that LLMs have real potential in anomaly detection, opening exciting avenues for future development. Imagine LLMs that can not only detect anomalies but also explain their reasoning in a clear, insightful way. This could revolutionize how we approach anomaly detection across various fields.