Tabular Data

Tabular data refers to structured information organized in rows and columns, such as spreadsheets, CSV files, and database tables. In AI governance contexts, tabular data presents distinct compliance challenges because it often contains sensitive business information, personal data, and metrics used to train or evaluate machine learning models. Organizations must implement proper data cataloging, access controls, and audit trails for tabular datasets to ensure they meet regulatory requirements like GDPR and support responsible AI practices.

1 item

ResearchGlobal2026-08-04

LLMs Fail on High-Dimensional Tabular Data, Exposing Fitness-for-Purpose Gaps

Researchers Marta Garnelo and Wojciech Czarnecki published findings showing that LLM accuracy degrades systematically as input dimensionality increases on tabular prediction tasks, while classical baselines hold flat or improve. The study tested five hypotheses across 31 benchmark datasets using a frontier LLM with no fine-tuning. Organizations using LLMs for fraud detection, risk scoring, or compliance monitoring on structured enterprise data face a direct fitness-for-purpose exposure.

LLM limitations model evaluation tabular data fitness-for-purpose fraud detection risk scoring model validation