On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective

TL;DR

This paper explores the expressiveness of softmax attention by deriving its recurrent form, revealing why it outperforms linear attention methods in neural network models.

Contribution

It introduces a recurrent neural network perspective of softmax attention, explaining its superior expressiveness compared to linear attention methods.

Findings

Softmax attention can be represented as a recurrent neural network.

Ablation of components reveals their roles in attention expressiveness.

Softmax attention's nonlinearity contributes to its higher accuracy.

Abstract

Since its introduction, softmax attention has become the backbone of modern transformer architectures due to its expressiveness and scalability across a wide range of tasks. However, the main drawback of softmax attention is the quadratic memory requirement and computational complexity with respect to the sequence length. By replacing the softmax nonlinearity, linear attention and similar methods have been introduced to avoid the quadratic bottleneck of softmax attention. Despite these linear forms of attention being derived from the original softmax formulation, they typically lag in terms of downstream accuracy. While strong intuition of the softmax nonlinearity on the query and key inner product suggests that it has desirable properties compared to other nonlinearities, the question of why this discrepancy exists still remains unanswered. This work demonstrates that linear attention is an approximation of softmax attention by deriving the recurrent form of softmax attention. Using this form, each part of softmax attention can be described in the language of recurrent neural networks (RNNs). Describing softmax attention as an RNN allows for the ablation of the components of softmax attention to understand the importance of each part and how they interact. In this way, our work helps explain why softmax attention is more expressive than its counterparts.