How does the number of layers in the Transformer affect the model's ability to capture long-range dependencies?

Increasing the number of layers in a Transformer model generally enhances its ability to capture long-range dependencies, albeit with diminishing returns and increased computational cost. Each layer in the Transformer, through its self-attention mechanism, can directly attend to any other word in the input sequence. However, in practice, the lower layers often focus on capturing local dependencies, such as syntactic relationships between adjacent words. As the data progresses through the network, higher layers can then ....