What is the transformer architecture?

The transformer architecture revolutionized NLP tasks by introducing a model that relies entirely on attention mechanisms, dispensing with recurrence entirely. Its core innovation, the self-attention mechanism, allows the model to focus on different parts of the input sequence when producing an output, thus understanding context better.

In more detail:

• Self-Attention Mechanism: This component calculates the attention score for each word in the sequence relative to every other word. It involves creating three matrices: Query (Q), Key (K), and Value (V). The attention score for a word is obtained by computing the dot product of the query with all keys and applying a softmax function to obtain weights, which are then used to combine the values. This mechanism allows the model to capture dependencies regardless of their distance in the sequence.

• Positional Encoding: Since transformers do not inherently understand the order of words, positional encoding is added to input embeddings to provide information about the position of words in the sequence. This is typically achieved using sine and cosine functions of different frequencies, which are added to the input embeddings.

• Feed-Forward Neural Network: Each position's output from the self-attention layer is passed through a feed-forward neural network. This is usually a fully connected network applied separately and identically to each position, allowing complex transformations of the attended input.

• Encoder-Decoder Structure: The transformer is composed of stacked encoders and decoders. The encoder processes the input sequence, while the decoder generates an output sequence. Each encoder consists of a self-attention layer and a feed-forward neural network, followed by normalization layers. The decoder includes an additional layer for processing encoder outputs.

Practical Applications: Transformers have been widely adopted in NLP for tasks such as language translation (e.g., Google Translate), text summarization, and automated text generation (e.g., GPT models).

For a deeper dive into the transformer, the original paper Attention is All You Need provides comprehensive insights into its architecture and workings.