Comment by wolttam
1 month ago
I think the main challenge with combining layers of different would models be their differing embedding sizes and potentially different vocabularies.
Even between two models of identical architecture, they may have landed on quite different internal representations if the training data recipe was substantially different.
But it would be fun to experiment with.
Even with the same embedding sizes and vocabularies, there’s nothing that forces the meaning of dimension 1 of model 1 to mean the same thing as dimension 1 of model 2 — there are lots of ways to permute the dimensions of a model without changing its output, so whatever dimension 1 means the first time you train a model is just as likely to end up as dimension 2 the second time you train is as it is to be consistent with the first model.
Nobody here or on Reddit has mentioned this, maybe bc it’s too obvious, but it’s clear to me that the residual connections are an absolutely necessary component to making this merging possible — that’s the only reason dimension 1 of a later layer is encouraged to mean something similar to dimension 1 of an earlier layer.
On a related note - would it be easier, instead of doing a benchmark sweep across the whole NxN set of start-end pairs for which layers to modify, to instead measure cross-correlation between outputs of all layers? Shouldn't that produce similar results?