Increasing transformer token length with a Maximum Entropy Principle Method

R. I. Cukier

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arXiv · 2408.10277

Increasing transformer token length with a Maximum Entropy Principle Method

R. I. Cukier

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Abstract

Transformers suffer from the computational overhead of their quadratic dependence on the length of sequences processed. We present three methods, all adding an intermediate step between training and inference/generation, which extend the autoregressive length of transformers. All rely on a Maximum Entropy Principle (MEP) whereby entropy is maximized in the presence of suitable constraints, accounted for by use of Lagrange Multipliers. These constraint methods extend the autoregressive character from T to 2T tokens in a linear-with-T fashion. There is overhead associated with this added step, but they should still be faster than the standard methods.

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R. I. Cukier. 2024-08-17. Increasing transformer token length with a Maximum Entropy Principle Method. https://arxiv.org/abs/2408.10277

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