In the context of transformers, which factor is most crucial for scaling self-attention to large datasets?

In the context of transformers, the most crucial factor for scaling self-attention to large datasets is the computational complexity.

Here's a step-by-step explanation:

1. Transformers use self-attention mechanism, which allows them to consider the entire input sequence simultaneously and weigh the importance of different elements in the sequence.

2. However, the self-attention mechanism has a computational complexity of O(n^2), where n is the length of the input sequence. This means that the computation time increases quadratically with the size of the input sequence.

3. Therefore, when dealing with large datasets, the computational complexity of the self-attention mechanism becomes a bottleneck. It can make the training process extremely slow and require a large amount of memory.

4. Various methods have been proposed to reduce the computational complexity of the self-attention mechanism, such as sparse attention, local attention, and low-rank approximations. These methods aim to approximate the full self-attention mechanism with a lower computational complexity, making it feasible to scale transformers to large datasets.

5. Hence, managing the computational complexity is the most crucial factor for scaling self-attention to large datasets.