LLMs can’t outperform a technique from the 70s, but they’re still worth using — here’s why

VentureBeat October 13, 2024
Kalyan Veeramachaneni, MIT Data to AI Lab, Sarah Alnegheimish, MIT Data to AI Lab

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...

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