A Transformer processes language by using self-attention to determine which words or tokens are most relevant to one another, even when they are far apart in a sentence.
First, the input text is divided into tokens and converted into numerical embeddings. The Transformer then creates three representations for each token: Query (Q), Key (K), and Value (V).
Self-attention compares each token's Query with the Keys of other tokens to calculate attention scores. These scores determine how much importance each token should give to the others. The weighted Values are then combined to create a context-aware representation.
For example, in the sentence “The car stopped because it was damaged,” self-attention can learn that “it” is strongly related to “car.” This helps the model understand relationships that depend on context rather than simply processing words individually.
A Transformer typically uses multi-head attention, where several attention mechanisms operate in parallel. Each head can learn different relationships, such as grammar, word associations, or long-range dependencies.
The Transformer also uses positional information so it can distinguish the order of tokens, followed by feed-forward layers that further transform the information.
In simple terms, self-attention lets every token look at other relevant tokens and decide how much each one matters, allowing Transformers to build a rich understanding of context and relationships throughout a sentence.
