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Comment by RossBencina | Hacker News Reader

I think that's the intuition behind MoE (Mixture of Experts). Train separate subnets for different tasks, train a router that selects which subnets to activate at inference time. Mixtral is a current open model which I believe implements this.

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No. MoE tends to change expert every other word. There’s a bit of pattern (like a lot of punctuation to one expert) but it’s not clear what. Nobody understands how or why the router chooses the expert. It’s so early.

> Nobody understands how or why the router chooses the expert. It’s so early.

Nobody understand how LLM works either. Is LLM as "early" as MoE ?

LLMs are really well understood, what do you mean? You can see the precise activations and token probabilities for every next token. You can abliterate the network however you'd like to suppress or excite concepts of your choosing.

There's various layers of understanding.

If you will excuse analogy and anthropomorphism, the human analogy of what we do and don't understand about LLMs is, I think, that we understand quantum mechanics, cell chemistry, and overall connectivity (perceptrons, activation functions, and architecture) and group psychology (general dynamics of the output), but not specifically how some belief is stored (in both humans and LLMs).

Mathematically speaking LLMs have very precise formulation and can be seen as F(context, X0, X1, ..., XP) = next_token. What science behind the LLMs is still lacking is how all these parameters are correlated one to each other and why one set of values is giving a better prediction than the other set of values. Right now, we arrive to these values through experimental approach, that is, through trainings.

i think the younger generation who came up post deep learning, has a very very low bar for “understanding” because they never knew a world where SotA models worked in a way that made sense.

> MoE tends to change expert every other word

Any citation on this one?

It's covered in the original Mistral "Mixtral of Experts" paper [0].

0. https://arxiv.org/abs/2401.04088

I believe it's actually a per token routing, not a "every few words"

It's mechanically capable of per-token routing, but the routing tends to be stable across more than one token. It's weird.

It's got nothing to do with words, and many MoEs route to multiple experts per token (the well known Mixtral variants for example activates 2 experts per token).

Weirdly it does have to do with words, but not intentionally. Mechanically the routing is per-token, but the routing is frequently stable across a word as an emergent property. At least, that's how I read the mixtral paper.

Yep.

Also note that I’m ELI5 so saying word is fine.

This is not how MoEs work at all. They are all trained together, often you have multiple experts activated for a single token. They are not domain specific in any way that is understandable by humans.

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