Selective Attention Improves Transformer
Authors: name: Yaniv Leviathan, name: Matan Kalman, name: Yossi Matias

Unneeded elements in the attention's context degrade performance. We
introduce Selective Attention, a simple parameter-free change to the standard
attention mechanism which reduces attention to unneeded elements. Selective
attention improves language modeling performance in a variety of model sizes
and context lengths. For example, a range of transformers trained with the
language modeling objective on C4 with selective attention perform equivalently
to standard transformers with ~2X more heads and parameters in their attention
modules. Selective attention also allows decreasing the size of the attention's
context buffer, leading to meaningful reductions in the memory and compute
requirements during inference. For example, transformers with 100M parameters
trained on C4 with context sizes of 512, 1,024, and 2,048 need 16X, 25X, and
47X less memory for their attention module, respectively, when equipped with
selective attention, as those without selective attention, with the same
validation perplexity.