Please help me with intuition for the transformer residual stream?:
Hi,
I have read the literature but could use a bit more intuition about why transformers are so well suited to interpretability. I get that you can decompose the residual stream by looking at how attention reroutes information, and I understand that rather than directly passing forward the MLPs’ outputs, the outputs are instead linearly combined with the stream...
But if this sequential adding to a shared vector scheme is so useful for learning, why don’t so many deep architectures like vanilla MLPs and CNNs or AlexNet have residual streams?
Please help me with intuition for the transformer residual stream?:
Hi,
I have read the literature but could use a bit more intuition about why transformers are so well suited to interpretability. I get that you can decompose the residual stream by looking at how attention reroutes information, and I understand that rather than directly passing forward the MLPs’ outputs, the outputs are instead linearly combined with the stream...
But if this sequential adding to a shared vector scheme is so useful for learning, why don’t so many deep architectures like vanilla MLPs and CNNs or AlexNet have residual streams?
Thanks