Self-Attention

Self-Attention is a key component of the Transformer architecture that allows models to weigh the relative importance of different parts of an input sequence when processing each element. It enables models to capture long-range dependencies and complex relationships within the data more effectively than previous methods.

By calculating attention scores for every word or token in a sentence against all others, the model can “attend” to relevant context regardless of its position. This breakthrough paved the way for the development of massively scalable and high-performance language models.