TFMs are built to treat tabular data as it is intended to be treated, namely that numeric data is treated like numbers. You also need to be aware of the upsides and downsides of when and how to use these powerful new AI models. Difficulties With Tabular Data People are often tempted to use LLMs to aid in exploring tabular data. The TFM could be shaped to support any domain of interest, such as financial tabular data, health-related tabular data, inventory tabular data, customer service tabular data, etc. “Tabular data constitutes the backbone of enterprise data infrastructure and powers a significant fraction of critical predictive machine learning applications.”