Why Large Language Models Fail at Tabular Prediction

A new research paper explores why large language models struggle to perform predictive analytics on tabular data, despite their success in other machine learning tasks. This limitation highlights a significant gap in the practical application of these models, as tabular data remains a standard requirement for most business and scientific workloads. The study suggests that while models excel at language, they currently lack the specific architectural frameworks needed to interpret and analyze structured numerical datasets effectively.