Transformers process sequences by replacing recurrence with a pattern of attention, positional signals, and feed-forward transforms that let every position in the input interact directly with every other. The architecture, introduced by Ashish Vaswani and colleagues at Google Brain in the paper Attention Is All You Need, reframed sequence modeling away from step-by-step state updates toward a global computation that assigns dynamic importance to tokens. This change addresses core limitations of earlier recurrent networks, where information had to travel through many time steps and could degrade or become slow to train.
Self-attention and positional information
At the heart of the transformer is self-attention, a mechanism that computes for each token a weighted sum of representations of all tokens in the sequence. The weights arise from pairwise similarity scores between token queries and keys; those scores let the model emphasize relevant distant tokens when computing a token’s new representation. Multi-head attention runs several attention computations in parallel, projecting inputs into different subspaces so the model can capture multiple types of relationships simultaneously. Because self-attention alone is agnostic to order, transformers add positional encodings to token embeddings so the model can infer sequence order. These encodings may be fixed sinusoidal patterns or learned vectors; both approaches supply the necessary information for distinguishing permutations of the same tokens, which is crucial for language, genomics, and time series.
Training paradigms and practical consequences
Different training objectives shape how transformers use sequence context. BERT, developed by Jacob Devlin at Google AI Language, trains with masked language modeling so the model learns bidirectional context by predicting masked tokens from surrounding tokens. Autoregressive variants, commonly used for generation, predict the next token given previous tokens and enforce a causal mask in attention. Architecturally, transformers stack alternating attention and feed-forward layers, with residual connections and layer normalization to stabilize gradients and enable deep models to be trained effectively.
The shift to attention-based sequence processing has practical consequences. Because attention compares all token pairs, transformers permit direct, short paths between distant elements, improving long-range dependency modeling. They also enable substantial parallelization across sequence positions during training, accelerating throughput on modern hardware compared with strictly sequential recurrent processing. However, attention’s full pairwise computation scales quadratically with sequence length, creating memory and compute bottlenecks for very long inputs; this trade-off has motivated many follow-on methods that approximate or sparsify attention.
These architectural choices have broader implications. Improved ability to model context enhances translation, summarization, and scientific sequence analysis, but large transformer training carries environmental and economic costs due to energy and hardware use. Models also reflect the cultural and territorial contours of their training data, making careful dataset curation and evaluation essential for safe, equitable deployment. Understanding how transformers process sequence information helps practitioners choose architectures and training strategies that balance accuracy, efficiency, and social responsibility.