In this paper, we propose that the dot product pairwise matching attention layer, which is widely used in Transformer-based models, is redundant for the model performance. Attention, in its original formulation, has to be seen rather as a human-level tool to explore and/or visualize relevancy scores in sequential data. However, the way how it is constructed leads to significant computational complexity. Instead, we present SimpleTRON: Simple Transformer with O(N) Complexity, a simple and fast alternative without any approximation that, unlike other approximation models, does not have any architecture-related overhead and therefore can be seen as a purely linear Transformer-like model. This architecture, to the best of our knowledge, outperforms existing sub-quadratic attention approximation models on several tasks from the Long-Range Arena benchmark. Moreover, we show, that SimpleTRON can benefit from weight transfer from pretrained large language models, as its parameters can be fully transferable.
@article{arxiv.2111.15588,
title = {SimpleTRON: Simple Transformer with O(N) Complexity},
author = {Uladzislau Yorsh and Alexander Kovalenko and Vojtěch Vančura and Daniel Vašata and Pavel Kordík and Tomáš Mikolov},
journal= {arXiv preprint arXiv:2111.15588},
year = {2022}
}