LLMs can't outperform a technique from the 70s, but they're still worth using — here's why
This year, our team at MIT Data to AI lab decided to try using large language models (LLMs) to perform a task usually left to very different machine learning tools — detecting anomalies in time series data. This has been a common machine learning (ML) task for decades, used frequently in industry to anticipate and find problems with heavy machinery. We developed a framework for using LLMs in this context, then compared their performance to 10 other methods, from state-of-the-art deep learning tools to a simple method from the 1970s called autoregressive integrated moving average (ARIMA). In the end, the LLMs lost to the other models in most cases — even the old-school ARIMA, which outperformed it on seven datasets out of a total of 11.