Why Large Language Models Fail at Tabular Prediction
sbulaev · hn · 2026-08-04
This paper investigates why Large Language Models (LLMs) struggle with tabular prediction tasks. The research highlights the inherent limitations and performance bottlenecks of LLMs when processing structured tabular data, especially when compared to traditional machine learning methods.
More from Research
- Trained two 16M-param models to do generative CAD with real physics — debreuil · 2026-08-24
- Claude model helps discover complex structure on S^6, solving 60-year-old math problem — Singularitarian · 2026-08-24
- Study: Agents read instructions/notes 60.5% of the time, rarely touch API docs — dair_ai · 2026-08-24
- Claude Verifies 43 Lean Modules autonomously, Tackling Theoretical Physics — Tkaraletsos · 2026-08-24
- AI fakes memory: why it gets confidently wrong without forgetting — PrajwalTomar_ · 2026-08-24
- Google's AI research agents discover 66 novel biomarkers in automated biomedical study — imjustnewatai · 2026-08-24