LLM Limitations

Large Language Model limitations refer to the inherent constraints and failure modes of LLMs, including hallucinations, outdated training data, context window restrictions, and poor performance on specialized tasks. Understanding these limitations is critical for enterprise AI governance because it directly impacts risk assessment, responsible deployment decisions, and the establishment of appropriate guardrails for business-critical applications. Organizations must document and communicate LLM limitations to stakeholders to set realistic expectations and ensure that AI systems are not deployed in contexts where their weaknesses could cause significant harm or regulatory violations.

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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