Transformers learn contextual token dependencies using a mechanism called self-attention, which allows the model to weigh the importance of each word in a sentence relative to others. This differs from traditional sequence models like RNNs or LSTMs, which process data sequentially and often struggle with long-range dependencies.

In a transformer, each input token is first converted into an embedding, a dense vector representing the token’s meaning. These embeddings are then enriched with positional encodings to preserve the word order, since transformers don’t process inputs sequentially by default.

The core of the transformer’s ability to understand context lies in the multi-head self-attention mechanism. In self-attention, every word (token) attends to every other word in the sequence to determine which ones are most relevant to its meaning. For example, in the sentence “The cat sat on the mat because it was tired,” the word “it” refers to “the cat.” Self-attention helps the model learn this dependency by assigning higher attention scores between “it” and “cat.”

Each self-attention layer computes three vectors—query (Q), key (K), and value (V)—from the input embeddings. The attention scores are derived from the dot product of Q and K, which are then used to weight the V vectors. This weighted sum represents the output for each token, capturing both its own meaning and its relation to others.

Multiple attention heads allow the model to focus on different aspects of the sentence simultaneously. These outputs pass through feed-forward layers and layer normalization, enabling the model to learn complex language patterns.

Because of this architecture, transformers can efficiently model both local and global token relationships, which is why they underpin models like BERT and GPT.

To dive deeper into these concepts, consider enrolling in a Generative AI certification course.