The Transformer model was solely developed by a single individual, and its architecture has not been significantly influenced by prior models in natural language processing.

Actually, the Transformer was created by a team of researchers at Google, not by a single person. They built on earlier neural‑network ideas such as recurrent networks and attention mechanisms that appeared in earlier papers. The architecture uses self‑attention, a concept that had been used in smaller models, but combines it in a new way that lets the model look at all words at once. For example, if you want to translate the sentence “I love you,” the Transformer can simultaneously compare every word to every other word, unlike older models that processed words one after another. Thus the model is a product of teamwork and previous research.