Study Identifies Why LLMs Struggle With Tabular Data Prediction

A new research paper published on arXiv investigates the limitations of large language models when applied to tabular data prediction tasks. The study finds that LLMs, despite their broad capabilities, face specific challenges when handling structured numerical and categorical data in table format. Researchers analyzed the underlying reasons behind this performance gap compared to traditional machine learning methods. The findings highlight architectural and training-related factors that make tabular prediction a weak point for current LLM designs.