The Transformer architecture revolutionized NLP and became the foundation for virtually all modern large language models. It replaced recurrent architectures with self-attention, enabling parallel processing and better capture of long-range dependencies.
Key Innovations
- Self-Attention: Allows each position to attend to all positions
- Positional Encoding: Injects sequence order information
- Multi-Head Attention: Learns multiple attention patterns simultaneously
- Feed-Forward Networks: Processes attended information
Impact
Transformers power GPT, BERT, T5, Claude, and most state-of-the-art language models. They’ve also been adapted for computer vision (ViT), speech, and multimodal tasks.
Tags
Related Terms
BERT
Bidirectional Encoder Representations from Transformers - a model that understands context by looking at text from both directions.
Encoder-Decoder
A architecture where the encoder processes input and the decoder generates output, used in translation and sequence-to-sequence tasks.
GPT
Generative Pre-trained Transformer - an autoregressive language model architecture that predicts the next token given previous context.
Multi-Head Attention
Running multiple attention operations in parallel with different learned projections, capturing diverse relational patterns.
Self-Attention
A mechanism where each token attends to all other tokens in the sequence to understand contextual relationships.