Most business prediction problems do not arrive as prose. They arrive as rows: account attributes, transactions, sensor readings, test results, and a target column. For this kind of data, gradient-boosted trees and other conventional methods remain hard to displace.
A new paper, Why Large Language Models Fail at Tabular Prediction, asks a much more useful question than “can an LLM classify a table?”: what, specifically, breaks as the task becomes more like ordinary tabular machine learning?
The answer from the authors’ controlled experiments is input dimensionality. Their result matters because it separates a real limitation from several explanations that sound plausible but did not hold up in their tests.
The experiment was about prediction, not table chat
The paper evaluates frontier LLMs in a pure inference setup: a model receives labeled examples and must predict labels for new rows in a single generation pass. There is no fine-tuning, retrieval pipeline, tool calling, or agent loop to compensate for the base model. This is deliberately narrow. It asks whether a general-purpose language model can act as a direct tabular learner.
