Efficient Attention Mechanisms for Large Language Models: A Survey

Understand

Transformer-based architectures have become the prevailing backbone of large language models.

• However, the quadratic time and memory complexity of self-attention remains a fundamental obstacle to efficient long-context modeling.
• To address this limitation, recent research has introduced two principal categories of efficient attention mechanisms.
• Linear attention methods achieve linear complexity through kernel approximations, recurrent formulations, or fastweight dynamics, thereby enabling scalable inference with reduced computational overhead.