The Structural Challenge: Why Language Models Fail with Tabular Data

Authors

  • Thirunaavukkarasu Murugesan

Keywords:

Tabular Data Processing, Structural Awareness, Language Model Limitations, Hybrid Neural Architectures, Table-Specialized Embeddings

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

References

Xiaokang Zhang et al., "TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios," arXiv:2403.19318v3. [Online]. Available: https://arxiv.org/html/2403.19318v3

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Published

2026-01-09

How to Cite

Thirunaavukkarasu Murugesan. (2026). The Structural Challenge: Why Language Models Fail with Tabular Data. Journal of Computational Analysis and Applications (JoCAAA), 35(1), 181–195. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4659

Issue

Vol. 35 No. 1 (2026): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.