Decoder Architecture › Summary

The decoder architecture powers modern language generation by combining self-attention with causal masking to enforce left-to-right information flow. Key takeaways:

• Causal masking prevents each position from attending to future positions, matching the autoregressive generation process
• Decoder blocks consist of masked self-attention and feed-forward layers, each with residual connections and normalization
• GPT-style models stack decoder blocks between embedding and language model head layers, forming a unified architecture for understanding and generation
• Layer stacking builds increasingly abstract representations, with each layer refining outputs from the previous layer
• KV caching enables efficient generation by storing key-value pairs from previous positions
• Generation proceeds token by token, with each new token conditioned on all previous ones through the attention mechanism

The decoder-only design has become the default architecture for large language models. Its single stack simplifies scaling and supports a wide range of language tasks. Encoder and encoder-decoder architectures retain advantages for specific tasks, but the decoder's unified approach to understanding and generation has proven effective across many language AI applications.