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arXiv · 2410.01600

ENTP: Encoder-only Next Token Prediction

Ethan Ewer·Daewon Chae·Thomas Zeng·Jinkyu Kim·Kangwook Lee
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Abstract

Next-token prediction is conventionally done using decoder-only Transformers with causal attention, as this approach allows for efficient reuse of keys and values. What if we were not compute-limited, should we still use decoder-only Transformers? In this work, we introduce Encoder-only Next Token Prediction (ENTP). We explore the differences between ENTP and decoder-only Transformers in expressive power and complexity, highlighting potential advantages of ENTP in settings with unbounded compute. We introduce the $\operatorname{Count3}$ task and show, both theoretically and experimentally, that while ENTP can perform this task easily, a decoder-only Transformer cannot. Finally, we empirically demonstrate the superior performance of ENTP across representative tasks where next-token prediction based Transformers can be evaluated, including addition, in-context learning, and language modeling.

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Ethan Ewer, Daewon Chae, Thomas Zeng, Jinkyu Kim, Kangwook Lee. 2024-10-02. ENTP: Encoder-only Next Token Prediction. https://arxiv.org/abs/2410.01600

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