What distinguishes the Transformer architecture from previous models in handling sequential data?

The Transformer uses self‑attention to look at all words in a sentence at once, rather than processing them one after another as in RNNs or LSTMs. Because every word can directly “talk” to every other word, the model learns long‑range relationships quickly and can be trained in parallel on a GPU. It adds a positional encoding to tell the model the order of words, so the sequence still matters. This lets the Transformer capture dependencies across the whole sentence without the slow, step‑by‑step recurrence that earlier models used. For example, when translating “the cat sat on the mat,” the Transformer can immediately relate “cat” and “mat” even though they are far apart in the sentence.