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 combine these local relationships to capture more global and long-range dependencies. With more layers, the model has more opportunities to learn these complex relationships and to propagate information across longer distances in the input sequence. Each layer can refine the representations learned by the previous layers, allowing the model to build a more complete and nuanced understanding of the relationships between words. For instance, a lower layer might identify that "cat" is a noun and "sat" is a verb, while a higher layer might use this information to understand that "cat" is the subject of the verb "sat", even if there are several words separating them. However, increasing the number of layers also increases the model's complexity and memory footprint, and can make training more difficult. The risk of overfitting also increases with the number of layers, especially when the training data is limited. There is also a diminishing returns effect; adding more layers beyond a certain point may not lead to significant improvements in performance. Therefore, the number of layers is typically chosen based on the specific task, the available resources, and the desired trade-off between accuracy and efficiency.