In this paper, the authors present a Transformer attention model with linear complexity that is mathematically proven to be Turing complete (and thus as powerful as the original quadratic attention model) and achieves new state-of-the-art results on many NLP tasks involving long sequences (e.g. question answering and summarization), as well as genomics data.
This paper suggests an approximate way of calculating self-attention in Transformer architectures that has linear space and time complexity in terms of the sequence length, with the resulting performance on benchmark datasets similar to that of the RoBERTa model based on the original Transformers with much less efficient quadratic attention complexity.
Contrary to the common consensus that self-attention is largely responsible for the superior performance of Transformer models on various NLP tasks, this paper suggests that substituting outputs of self-attention layers with random or simply synthesized data is sufficient to achieve similar results with better efficiency.
This paper describes a new training approach for Transformer network architectures used for language modeling tasks. The authors demonstrate that their technique results in greatly improved training efficiency and better performance on common benchmark datasets (GLUE, SQuAD) compared to other state-of-the-art NLP models of similar size.