Transformer Architecture

The Transformer architecture, introduced by Vaswani et al. in 2017 with the seminal paper "Attention is All You Need," represents a neural network architecture based entirely on self-attention mechanisms without relying on recurrence or convolution. Transformers have revolutionized sequence modeling, becoming the foundation for modern large language models like GPT and BERT, and increasingly extending to computer vision and multimodal applications. Unlike recurrent networks that process sequences step-by-step, Transformers process all sequence positions simultaneously, enabling dramatically more efficient training and inference through parallelization.​

The revolutionary innovation enabling Transformers is the self-attention mechanism, which allows each position in a sequence to directly compare and weight its importance against all other positions. Rather than information flowing linearly through layers as in RNNs, self-attention enables direct long-range dependencies regardless of distance—a neuron processing word N can immediately attend to word 1 without information degrading across intermediate layers.​

Self-attention operates through query, key, and value components computed from input representations. For each position, the network computes query vectors asking "what information do I need," key vectors providing "what information am I," and value vectors containing "the actual information." Attention weights computed between query-key pairs determine which value information flows to each position.​

Transformers consist of encoder and decoder components, though modern applications often use only one. The encoder processes input sequences to create rich contextual representations, while the decoder generates output sequences, typically using encoder outputs to guide generation.​

Multi-head attention enables the network to simultaneously attend to information from different representation subspaces, allowing the model to focus on different aspects of input simultaneously—essential for capturing complex relationships. Multiple parallel attention mechanisms operate independently, then concatenate outputs before passing to subsequent layers.​

Positional Encoding: Unlike CNNs and RNNs which have built-in positional awareness, Transformers require explicit positional encoding representing token positions, enabling the network to understand sequence order.​

Transformers offer transformative advantages over prior architectures:

Parallelization: All positions process simultaneously rather than sequentially, enabling efficient batch training on modern hardware. This dramatically reduces training time—the original Transformer paper achieved state-of-the-art machine translation results in 3.5 days on eight GPUs.​

Long-Range Dependencies: Self-attention directly connects all positions, avoiding gradient vanishing in deep networks and enabling learning of patterns across long distances.​

Interpretability: Attention weights provide interpretable insights into which parts of input the model considers important.​

Transformers enabled breakthrough language models:

BERT (Bidirectional Encoder Representations from Transformers): Uses transformer encoders with bidirectional self-attention to create powerful representations for understanding tasks like question answering and sentiment analysis, enabling fine-tuning with minimal data.​

GPT Series: Employs transformer decoders with unidirectional attention for generation tasks, achieving remarkable zero-shot and few-shot learning capabilities.​

T5, BART, and Variants: Combine encoders and decoders for versatile text-to-text tasks.​

These models typically employ pre-training on massive unlabeled corpora followed by fine-tuning on specific tasks—a paradigm enabling state-of-the-art performance on diverse applications.​

Vision Transformers (ViTs) extend Transformers to images by dividing images into patches, treating them as sequences, achieving competitive or superior performance to CNNs with greater efficiency.​

Transformers now enable multimodal models combining text, image, and other modalities.​

Quadratic Complexity: Self-attention's computational cost scales quadratically with sequence length, limiting application to very long sequences. Recent research explores linear-complexity variants.​

Interpretability Challenges: While attention weights provide some interpretability, understanding how Transformers combine information across layers and heads remains partially mysterious.​

Fine-Tuning Instability: Fine-tuning large pre-trained Transformers can be unstable, with performance varying across random seeds. Techniques like LoRA (Low-Rank Adaptation) enable more stable and efficient fine-tuning.