alt Hacker NewsNeural networks are non-linear, so I think you wouldn’t be able to compute typical eigenvalues. You could compute the eigenvalues and/or singular of the individual weight matrices (I’m sure this has been studied). SVDs are very conventional for making low-rank approximations, so it must have been studied.
The concept of nonlinear eigenvalues exists, but it is a bit more exotic.
I saw a presentation about this in 2022.
Someone found a way to get "something like" a tri-diagonal matrix that was equivalent to the LLM they were studying in 2022.
Apologies for being informal and hand-wavey. Been a long time and I probably forgot a few important points.