The Structural Challenge: Why Language Models Fail with Tabular Data
Abstract
This article investigates the fundamental architectural limitations that hold general-purpose largelanguage models back from effectively processing tabular data. Though these models excel atunstructured text tasks, they consistently underperform when confronted with tables because of inherent linear tokenization schemes and their lack of structural awareness
Fields of Study & Subjects
Topic Modeling
Multimodal Machine Learning Applications
Natural Language Processing Techniques
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 9 January 2026