How do transformers handle long-range dependencies in sequences?

Transformers handle long-range dependencies through their self-attention mechanism, which allows the model to weigh the relevance of all tokens in the input sequence, regardless of distance. Unlike recurrent neural networks (RNNs), which process tokens sequentially and may struggle with distant relationships, transformers can attend to all tokens simultaneously. This ability enables transformers to capture complex dependencies and context across long sequences effectively.