Comment by recursivegirth
7 days ago
I think this is where leveraging classifier models will become important. The frontier LLM models do "everything", while we've known for a while that to truly scale this we will need to distill models into their individual functions. I don't see this as necessarily a bad thing and hope more is done in this space. Very promising.
Bitter lesson is knocking. Mixture of experts is essentially what you’re describing but free from unnecessary inductive biases.
Mixture of Experts is absolutely not what they're describing. MoE has to be one of the most misleadingly named things ever. It's completely confusing as to what it actually is.
MoE is literally exactly what they're describing. The classifier being described is baked into the model and is the thing that makes it MoE.
Imagine I had [a model that was good at math], [model that was good at code], [model that was good at writing], [model that was good at general knowledge]. If I then had [a model that was good at determining whether the user query would be best served by one of those models and sent it to it, leaving the rest of the models inactive], that is the platonic version of what MoE is. In practice, it works a bit differently. It instead basically restricts the number of pathways that can be utilized in solving problems during training, which allows for "expert neuron groupings" to form and "classifier layers" to form earlier on in the structure, but the effect is the same (better, even, since it allows some overlap between structures of experts). It also allows "routing to an expert" to happen token-by-token rather than at the prompt level.
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Note the “but free from unnecessary inductive biases” part of my comment. By that I meant a decision to make each expert good at a human-defined thing.
1) Mixture of experts as an LLM architecture is not the same thing. In MoE each “expert” can activate for any given token.
2) Bitter lesson is misunderstood. Specialization and inductive biases still matter. ChatGPT isn’t the best chess player in the world just because it’s seen more math problems or read more Japanese poetry. Stockfish is, because it bakes in useful inductive biases like minimax.
There is value in splitting things. If all I ever do is local app automations, i don’t need model that knows how to code. If all I ever do is coding, i don’t need a model that translates english to slovakian.
Slovakia mentioned, let's gooo. Ehm, exactly, we can achieve better smaller models for specialized tasks rather than using compute to improve a big model that does everything. There's a lingering philosophical question if better language processing capabilities translate to better image processing capabilities (i.e. having the vocabulary and experience to properly describe an image), but I still think that identifying tasks and splitting responsibilities saves a lot of effort.
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There is value in splitting things but there is also a cost. You have to train the specialized model, for that you have to know your use case, you have to hope the use case is going to be stable over time, you then have to see if you can remove english -> slovakian or coding from a model without affecting the useful parts.
Good point! I thought you meant splitting them and then doing inference with some kind of learned router while keeping all the split models loaded at once. What you're suggesting is pretty sensible.
I enjoy doing local image generation and this is one thing that the community around that has really optimized.
In some workflows you might have 20 different models doing their specialized tasks. Pose detection, hand/eye/face detailers, classifiers, refiners, up scalers, taggers, etc can all use their own models and that’s not even the including the model(s) used for the actual image generation part.
I’m interested to see the optimization when this concept gets applied to other general ai tasks.