The Transformer architecture is a groundbreaking neural network model that revolutionized natural language processing. Introduced in the paper 'Attention Is All You Need' in 2017, it replaced traditional recurrent and convolutional approaches with a self-attention mechanism. The architecture consists of an encoder stack that processes input tokens and a decoder stack that generates output tokens, with attention mechanisms allowing the model to focus on relevant parts of the sequence.
The self-attention mechanism is the core innovation of the Transformer. It allows each token in the sequence to attend to all other tokens, computing attention scores that determine how much focus each token should give to every other token. This is achieved through Query, Key, and Value matrices. The attention formula computes a weighted sum where tokens with higher relevance receive more attention, enabling the model to capture long-range dependencies effectively.
The encoder stack is composed of six identical layers stacked on top of each other. Each encoder layer contains two main sub-layers: a multi-head self-attention mechanism and a position-wise feed-forward network. Around each sub-layer, residual connections and layer normalization are applied. The residual connections help with gradient flow during training, while layer normalization stabilizes the learning process. This design allows the encoder to build increasingly complex representations of the input sequence.
The decoder stack mirrors the encoder with six identical layers, but each decoder layer has three sub-layers instead of two. First is the masked multi-head self-attention, which prevents the decoder from attending to future tokens during training. Second is the multi-head cross-attention that allows the decoder to attend to the encoder's output. Third is the position-wise feed-forward network. The cross-attention mechanism is crucial as it enables the decoder to focus on relevant parts of the input sequence when generating each output token.
The Transformer architecture offers several key advantages that revolutionized natural language processing. First, it enables parallel processing during training, making it much faster than sequential models like RNNs. Second, it handles long-range dependencies more effectively through self-attention. Third, it eliminates the need for recurrence or convolution, simplifying the architecture. Fourth, it serves as the foundation for modern large language models like GPT and BERT. Finally, it's highly scalable, allowing for the creation of increasingly large and powerful models that have transformed AI capabilities.