Why LLMs Fail at Tabular Prediction: Dimensionality Is the Killer

Why Large Language Models Fail at Tabular Prediction

Large language models excel at many tasks but struggle with tabular data. A new study tests a frontier LLM in a pure inference setting, ruling out noise, CSV formatting, tokenization, and query size as culprits. Instead, dimensionality is decisive: as input dimensions increase, LLM accuracy drops while classical baselines stay flat or improve. The LLM's predictions resemble distance-based methods in 2D but diverge in higher dimensions, suggesting a fundamental limitation.