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Why Large Language Models Fail at Tabular Prediction
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Overview
Large language models (LLMs) struggle with predictive analytics over tabular data, which is a common machine learning workload. This limitation has led to the emergence of tabular foundation models aimed at addressing this gap.
Summary from arxiv.org.
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Hacker News · arxiv.org
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Based on 5 sampled comments from this thread. Analyzed 2026-08-04.
- Praise: The paper tackles a critical issue in AI, addressing why LLMs struggle with tabular data, which is vital for improving future models.
- Praise: Some commenters highlight that tree boosting methods are still the reigning champs for tabular tasks, showing the need for LLMs to evolve.
- Concern: Many see this research as niche and irrelevant to real-world applications, questioning its practical value.
- Concern: The consensus is that LLMs are fundamentally outclassed by older algorithms like tree boosting for tabular predictions, raising doubts about their versatility.