Why Large Language Models Fail at Tabular Prediction
Computer Science > Machine Learning arXiv:2608.02412 (cs) [Submitted on 3 Aug 2026] Title:Why Large Language Models Fail at Tabular Prediction Authors:Marta Garnelo, Wojciech M. This gap is the founding premise of the fast-growing field of tabular foundation models, but the question of why generic LLMs fail has remained open. Dimensionality, in contrast, is decisive: sweeping random linear projections of thirty-one benchmark datasets, the LLM is the only method among nine whose accuracy decreases as dimensionality grows, while every classical baseline stays flat or improves.
- ▪Computer Science > Machine Learning arXiv:2608.02412 (cs) [Submitted on 3 Aug 2026] Title:Why Large Language Models Fail at Tabular Prediction Authors:Marta Garnelo, Wojciech M.
- ▪This gap is the founding premise of the fast-growing field of tabular foundation models, but the question of why generic LLMs fail has remained open.
- ▪Dimensionality, in contrast, is decisive: sweeping random linear projections of thirty-one benchmark datasets, the LLM is the only method among nine whose accuracy decreases as dimensionality grows, while every classical baseline stays flat
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2608.02412 |
| Publication time | Tue, 04 Aug 2026 10:07:06 +0000 |
| Retrieval time | 2026-08-04T12:05:42.566Z |
| Last seen | 2026-08-04T12:05:42.566Z |
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Computer Science > Machine Learning arXiv:2608.02412 (cs) [Submitted on 3 Aug 2026] Title:Why Large Language Models Fail at Tabular Prediction Authors:Marta Garnelo, Wojciech M. Czarnecki View a PDF of the paper titled Why Large Language Models Fail at Tabular Prediction, by Marta Garnelo and 1 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning workloads: predictive analytics over tabular data. This gap is the founding premise of the fast-growing field of tabular foundation models, but the question of why generic LLMs fail has remained open.
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