[littlestymaar]

This doesn't match with the common knowledge on the topic, which is that model size is more important than the architecture. And training size is even more important, which is why single digit billion parameters are strongers than hundreds-of-billion ones from several years early when “Chinchilla optimal training” was in fashion.

SSM are literally the proof that all that really matters is training scalability.

The Universal approximation theorem doesn't care about the architecture after all.

[quantadev]

If you parse my words a bit more carefully, you'll realize to test my claim there's a simple thought experiment (or real experiment) you can do which is this:

Take our "current large size" (my words from last post) LLMs, as they are currently today, and then simply remove the Self-Attention wiring, and see if that destroys the emergent intelligence aspect or not. I claim it would. But at the same time this doesn't mean you can just stick Self-Attention onto a small model and expect intelligence to once again emerge.

[littlestymaar]

You are wildly overestimating the “emergent capabilities” of current models, and underestimate alternative architectures's (namely SSM) performance at the same size.

Also, performance of the modern “small” models show that your last sentence isn't really true either.

[quantadev]

> wildly overestimating the “emergent capabilities”

How could I be "overestimating" the emergent capabilities when I never even quantified those capabilities other than to call them "emergent" and impressive?

> “small” models show that your last sentence isn't true either.

I never said that even a perfect architecture would make small models "intelligent". However to the extent that even smaller LLMs can exhibit surprising capabilities, that's more evidence IN FAVOR OF everything I've said, not evidence against.

EDIT: But in that last sentence (of prior reply) by "small" what I meant was genuinely small, meaning non-LLM, and you seem to have interpreted it as "a smaller LLM"

[littlestymaar]

Even 1B parameters model show “impressive capabilities” for anyone not accustomed to the current state of the art. And there are plenty of relatively small models that perform as well as ChatGPT 3.5 when it was first released and felt like magic.

“All” that was needed to get there was “just” feeding it more data. The fact that we were actually able to train billion parameters models on multiple trillion tokens is the key property of the transformers, there's no magic beyond that (it's already cool enough though): it's not so much that they are more intelligent, it's simply that with them we can brute-force in a scalable fashion.

[quantadev]

Yes even the original Transformers model had only millions of parameters and nonetheless showed "impressive capabilities" because it also had Self-Attention.

If you know of any models that have had success (even at the GPT-2 level) without Self-Attention, I'd be interested to know what they are, because I don't know of any.

[littlestymaar]

RWKV.

There aren't many multi-billion-parameters non-transformer models because of path dependence, but that doesn't mean that only transformers can achieve this kind of results.