From Multi-Head to Latent Attention: A Deep Dive into Attention Mechanisms

This article explores the evolution of attention mechanisms in natural language processing, from the initial Multi-Head Attention (MHA) to more advanced variants like Multi-Latent Head Attention (MHLA). MHA weighs important words in context by calculating query, key, and value vectors; however, its computational and memory complexity grows quadratically with sequence length. To address this, newer approaches like MHLA emerged, improving computational speed and scalability without sacrificing performance – for example, by using KV caching to reduce redundant calculations. The article clearly explains the core concepts, advantages, and limitations of these mechanisms and their applications in models like BERT, RoBERTa, and Deepseek.