Before 2017, natural language processing was dominated by Recurrent Neural Networks (RNNs). These architectures processed text sequentially, word by word. This meant they were slow to train and struggled to retain context over long paragraphs.
Then came the 'Attention Is All You Need' paper, introducing the Transformer architecture. It discarded recurrence entirely. Instead, it processed all words in a sentence simultaneously, utilizing a mechanism called 'self-attention'.
The implications were profound. Because Transformers processed data in parallel, they could be trained on massive clusters of GPUs, allowing models to scale to unprecedented sizes. The era of Large Language Models had officially begun.
