Self-Attention is a key mechanism in Transformer models that allows them to focus on the most relevant parts of an input sequence when processing it. Instead of reading text word by word in order, self-attention evaluates the relationships between all words in the sequence simultaneously to understand context better.
How it works: For each word in the sequence, the model assigns’ attention scores to every other word, determining how much influence those words have on the current word’s meaning. These scores are used to weigh the importance of each word when creating a representation of the input.
Example: In the sentence “The cat sat on the mat, and it was soft”:
- Self-attention helps the model understand that “it” refers to “the mat,” not “the cat,” by analysing how words relate to one another.
Self-attention is what makes Transformers so powerful for tasks like language translation, summarization, and question-answering, as it enables them to capture context effectively.