How do transformer architectures differ from traditional recurrent neural networks (RNNs) in handling language data?

Unlike RNNs, which process data sequentially and can struggle with long-distance dependencies due to vanishing gradients, transformers use self-attention mechanisms to process all parts of the input data in parallel. This allows them to capture complex dependencies and significantly speeds up training by leveraging modern hardware architectures more effectively.