The emergence of the Transformer in advanced language models

In 2017, inspired by the capabilities of CNNs and the innovative application of attention mechanisms, Vaswani et al. introduced the transformer architecture in the seminal paper Attention is All You Need. The original transformer applied several novel methods, particularly emphasizing the instrumental impact of attention. It employed a self-attention mechanism, allowing each element in the input sequence to focus on distinct parts of the sequence, capturing dependencies regardless of their positions in a structured manner. The term “self” in “self-attention” refers to how the attention mechanism is applied to the input sequence itself, meaning each element in the sequence is compared to every other element to determine its attention scores.

To truly appreciate how the transformer architecture works, we can describe how the components in its architecture play a role in handling a particular task...