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arXiv · 2307.07843

Transformers are Universal Predictors

Sourya Basu·Moulik Choraria·Lav R. Varshney
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Abstract

We find limits to the Transformer architecture for language modeling and show it has a universal prediction property in an information-theoretic sense. We further analyze performance in non-asymptotic data regimes to understand the role of various components of the Transformer architecture, especially in the context of data-efficient training. We validate our theoretical analysis with experiments on both synthetic and real datasets.

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Sourya Basu, Moulik Choraria, Lav R. Varshney. 2023-07-15. Transformers are Universal Predictors. https://arxiv.org/abs/2307.07843

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