Attention Is All You Need

Overview. The Transformer paper proposes an encoder-decoder architecture based on attention without recurrent or convolutional sequence layers. Its design enables greater training parallelism while modeling token-to-token interactions directly.

Mechanism and context. The paper's encoder-decoder replaces recurrent and convolutional sequence layers with multi-head attention and position-wise computation. Direct token-to-token interactions shorten dependency paths and allow substantially more parallel work during training than a recurrent sequence requires.

Practical use. When reading a modern architecture, use the paper as a reference point and then list what changed: encoder or decoder structure, positional method, attention pattern, objective, and training scale. This avoids attributing every later design choice to the original Transformer.

Related reading. Read Transformer architecture for a system-level explanation, Scaled dot-product attention for the core calculation, Positional encoding for order information, and Language Models are Few-Shot Learners for a later scaling study.