Transformers
The Transformer is a deep learning architecture introduced in the landmark 2017 paper “Attention Is All You Need,” which revolutionized the field of natural language processing and beyond. Unlike previous recurrent models that process data sequentially, transformers process entire sequences of data in parallel, relying heavily on a mechanism called self-attention to weigh the significance of different parts of the input relative to one another. This parallelization allows for highly efficient training on modern hardware, enabling the creation of massive foundational models with billions of parameters. Transformers are the underlying architecture for state-of-the-art systems such as BERT, GPT, and modern image-generation models.
Key Concepts
- Self-Attention Mechanism: A process that allows the model to look at other words (or tokens) in the input sequence to better understand the context and relationships of the current word being processed.
- Encoder-Decoder Architecture: The original transformer consists of an encoder that maps the input sequence into a continuous representation and a decoder that generates the output sequence, though many modern variants (like GPT) use only the decoder part.
- Positional Encoding: Since transformers process data in parallel rather than sequentially, positional encodings are added to the input embeddings to provide information about the relative or absolute position of the tokens in the sequence.
- Multi-Head Attention: An extension of self-attention where the model runs multiple attention mechanisms in parallel, allowing it to jointly attend to information from different representation subspaces at different positions.