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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