Imagine a vast sea of data, numbers swirling in complex patterns. Hidden within this sea are anomalies—unusual data points that deviate from the norm. These anomalies can indicate anything from fraudulent activity to critical equipment failures. Traditionally, spotting these anomalies required complex algorithms and painstaking analysis. But what if we could teach AI to find them for us? Researchers are exploring the surprising ability of Large Language Models (LLMs), typically known for their text prowess, to detect anomalies in tabular data. The process involves transforming numerical data into a text format that LLMs can understand. Each data point is assigned an identifier and its value is represented as text, effectively turning the table into a series of sentences. The LLM is then prompted with a question like, "Which data points are abnormal?" The results are promising. Powerful LLMs like GPT-4 can identify anomalies with an accuracy comparable to state-of-the-art methods, even without specialized training. This zero-shot capability is a testament to the general knowledge and reasoning abilities encoded within these massive models. For LLMs that struggle with this task out-of-the-box, researchers have developed a clever solution: they create synthetic datasets of normal and anomalous data to fine-tune the LLMs. This training helps align the LLMs to the specific task of anomaly detection, significantly boosting their performance. The implications of using LLMs for anomaly detection are far-reaching. Imagine simplifying complex data analysis tasks, automating error detection in databases, or even identifying unusual patterns in medical data. However, challenges remain. LLMs can sometimes produce factual errors, highlighting the need for ongoing research to improve their reliability. As LLMs continue to evolve, their ability to find the 'odd one out' in our data will likely become an invaluable tool across various industries, from finance to healthcare and beyond.