A team of developers is working on a new language translation application. They are debating whether to use traditional RNNs or the Transformer architecture for their model. Based on the principles of the Transformer architecture, which of the following reasons should they prioritize when making their decision?

The team should prioritize the Transformer’s ability to model long‑range dependencies with self‑attention, which lets every word look at every other word in the sentence, giving better context than an RNN that only sees the previous step. They should also value the parallel computation of the Transformer, which allows much faster training on modern GPUs, while RNNs must process tokens sequentially. The Transformer’s multi‑head attention lets the model learn multiple patterns at once, improving translation quality. Finally, the Transformer’s positional encodings give the model explicit position information without needing a recurrent chain, making it easier to handle variable‑length sequences. For example, translating a sentence with a long phrase works better with attention because the model can directly relate distant words.