Why Softmax Attention Outperforms Linear Attention

Understand

Large transformer models have achieved state-of-the-art results in numerous natural language processing tasks.

• Among the pivotal components of the transformer architecture, the attention mechanism plays a crucial role in capturing token interactions within sequences through the utilization of softmax function.
• Conversely, linear attention presents a more computationally efficient alternative by approximating the softmax operation with linear complexity.
• However, it exhibits substantial performance degradation when compared to the traditional softmax attention mechanism.