The Transformer architecture has become a foundational architecture in the field of artificial intelligence (AI) and in the development of large language models (LLMs).
Introduced in the seminal paper "Attention Is All You Need" by Google AI researchers, the Transformer model outlined a novel concept described as a ‘self-attention mechanism’.
In contrast to models like RNNs and LSTMs that process data sequentially, the self-attention mechanism carries out a form of parallel data processing that allows it to better capture long-range dependencies across the input data.
This means that for any given element in the sequence, the mechanism simultaneously considers how this element relates to every other element, including those far away in the sequence.
To explain that in everyday language, self-attention mechanisms enable more accurate understanding of a sentence, or a longer passage of text, by considering all parts of the text input in relation to each other, simultaneously.