Understanding the Transformer Architecture
Introduction
Since its introduction in 2017, the Transformer architecture has become the cornerstone of natural language processing. This article provides an in-depth yet accessible explanation of Transformer’s core mechanisms.
Why Do We Need Transformers?
Before Transformers, RNNs and LSTMs were the mainstream methods for sequence modeling. However, they had several limitations:
- Sequential Computation - Cannot be parallelized, leading to low training efficiency
- Long-range Dependencies - Difficulty capturing long-distance contextual information
- Gradient Issues - Long sequences prone to vanishing gradients
Transformers elegantly solve these problems through the self-attention mechanism.