SimpleTron: Eliminating Softmax from Attention Computation

• URL: http://arxiv.org/abs/2111.15588v3
• Date: Thu, 2 Dec 2021 08:16:33 GMT
• Title: SimpleTron: Eliminating Softmax from Attention Computation
• Authors: Uladzislau Yorsh, Pavel Kord\'ik, Alexander Kovalenko
• Abstract summary: We propose that the dot product pairwise matching attention layer is redundant for the model performance. We present a simple and fast alternative without any approximation that, to the best of our knowledge, outperforms existing attention approximations on several tasks from the Long-Range Arena benchmark.
• Score: 68.8204255655161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 rather seen as a human-level tool to explore and/or visualize relevancy scores in the sequences. Instead, we present a simple and fast alternative without any approximation that, to the best of our knowledge, outperforms existing attention approximations on several tasks from the Long-Range Arena benchmark.

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