Invited Talk by Marta Garnelo: Tree-vial Pursuits: How Humble Decision Trees Still Outsmart the Generative Giants

Large Language Models have redefined our expectations for what AI can achieve, showing remarkable prowess in natural language, complex reasoning, and code synthesis. Given these leaps, it is tempting to assume that numerical fluency would follow as a natural byproduct of scale. However, the reality is far more humbling: even the most sophisticated LLMs often fail spectacularly at basic tabular prediction tasks. This gap is a significant bottleneck, considering that the vast majority of the world’s enterprise and scientific data remains locked in rows and columns. In this talk we investigate the dissonance between a model's linguistic confidence and its actual predictive performance and we explore where our false perception of the LLMs' numerical mastery could stem from.