Transformers are Universal Predictors

• URL: http://arxiv.org/abs/2307.07843v1
• Date: Sat, 15 Jul 2023 16:19:37 GMT
• Title: Transformers are Universal Predictors
• Authors: Sourya Basu, Moulik Choraria, Lav R. Varshney
• Abstract summary: We find limits to the Transformer architecture for language modeling and show it has a universal prediction property in an information-theoretic sense. We 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.
• Score: 21.92580010179886
• License: http://creativecommons.org/licenses/by/4.0/
• 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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