Transformer architecture was designed to solve a key problem in earlier AI models. Traditional models, like Recurrent Neural Networks (RNNs), struggled to process long sentences or complex texts because they worked word by word. Transformer architecture overcomes this limitation by considering the entire context of a sentence rather than processing each word individually.
Imagine you’re reading a book. To understand the story, you not only need to read individual words but also how they form sentences and convey meaning together. This is where the strength of transformer architecture comes into play, using a mechanism called self-attention. This allows the model to not only understand each word but also the relationships between all the words in a sentence, enabling it to analyze the full context all at once.