Aha, so it's decided per token, not per input. I thought at first the LLM chooses a "submodel" based on the input and then follows it to generate the whole output.
Thanks a lot.
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Yeah, this image helped solidify that for me.
https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_pr...
Each different color highlight is a generated by a different expert.
You can see that the "experts" are more experts of syntax than concepts. Notice how the light blue one almost always generates puncuation and operators. (until later layers when the red one does so)
I'm honestly not too sure the mechanism behind which experts gets chosen. I'm sure it's encoded in the weights somehow, but I haven't gone too deep into MoE models.
I see a whitespace expert, a punctuation expert, and a first word expert. It's interesting to see how the experts specialize.
Right?
Then you get some strange ones where parts of whole words are generated by different experts.
Makes me think that there’s room for improvement in the expert selection machinery, but I don’t know enough about it to speculate.
Honestly, looks like someone throwing spaghetti on a wall a billion times and seeing what sticks, then training the throwing arm to somehow minimize something. I get that LLM magic is kinda magic and is doing some cool stuff, but this looks like it's just chaos and statistical untangling that happens to minimize some random fitness function X-levels down the line.