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Why Large Language Models Fail at Tabular Prediction

Research LLMs & Foundation Models

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TL;DR - An arXiv study isolates why frontier LLMs underperform classical models on tabular prediction, finding that input dimensionality — not noise, formatting, tokenization, or batching — is the decisive failure factor. It matters because it grounds the case for dedicated tabular foundation models rather than generic LLM prompting.

  • Tested a frontier LLM in a pure inference regime: one generation pass over a prompt holding full train + test data, with no tools, agentic scaffolding, or fine-tuning.
  • Controlled experiments falsified four hypotheses: noisy/non-linearly-separable data, linearised CSV obscuring column structure, numeric tokenization, and number of test points per query.
  • Sweeping random linear projections across 31 benchmark datasets, the LLM was the only one of nine methods whose accuracy decreased with rising dimensionality; all classical baselines stayed flat or improved.
  • Behaviourally, the LLM mimics a local distance-based method in 2D (up to 91.6% grid agreement), but in higher dimensions none of 252 configured classical models — even with tuned dimension-dependent noise — reproduces its predictions; the authors explicitly leave the internal mechanism open.

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Why Large Language Models Fail at Tabular Prediction

arXiv cs.LG Marta Garnelo, Wojciech M. Czarnecki 2026-08-03 arXiv:2608.02412
Public signals Semantic Scholar citations 1 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 1 · Influential citations 0 X · N/A Fetched 2026-09-03 14:33:45.818844 UTC

TL;DR - An arXiv study isolates why frontier LLMs underperform classical models on tabular prediction, finding that input dimensionality — not noise, formatting, tokenization, or batching — is the decisive failure factor. It matters because it grounds the case for dedicated tabular foundation models rather than generic LLM prompting.

  • Tested a frontier LLM in a pure inference regime: one generation pass over a prompt holding full train + test data, with no tools, agentic scaffolding, or fine-tuning.
  • Controlled experiments falsified four hypotheses: noisy/non-linearly-separable data, linearised CSV obscuring column structure, numeric tokenization, and number of test points per query.
  • Sweeping random linear projections across 31 benchmark datasets, the LLM was the only one of nine methods whose accuracy decreased with rising dimensionality; all classical baselines stayed flat or improved.
  • Behaviourally, the LLM mimics a local distance-based method in 2D (up to 91.6% grid agreement), but in higher dimensions none of 252 configured classical models — even with tuned dimension-dependent noise — reproduces its predictions; the authors explicitly leave the internal mechanism open.
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