Comment by mnk47 | Hacker News Reader

Comment by mnk47 | Hacker News Reader

Yi Tay's response (chief scientist at Reka AI, ex-Google Brain researcher): https://twitter.com/YiTayML/status/1783273130087289021

>not true, especially for language. if you trained a large & deep MLP language model with no self-attention, no matter how much data you'll feed it you'll still be lacking behind a transformer (with much less data). will it get to the same point? i don't think so. your tokens cannot even see each other in a raw MLP.

>on the other hand, tiny tweaks to transformers may not matter as much as data/compute. sure. but it's also not very accurate to say "architecture research" does not matter and "makes no difference". i hear this a lot about how people use this to justify not innovating at the architecture level.

>the truth is the community stands on the shoulder of giants of all the arch research that have been done to push the transformer to this state today.

>architecture research matters. many people just take it for granted these days.

I'm James Betker.

Of course architecture matters in this regard lol. Comparing a CNN to a transformer is like comparing two children brought up in the same household but one has a severe disability.

What I meant in this blog post was that given two NNs which have the same basic components that are sufficiently large and trained long enough on the same dataset, the "behavior" of the resulting models is often shockingly similar. "Behavior" here means the typical (mean, heh) responses you get from the model. This is a function of your dataset distribution.

:edit: Perhaps it'd be best to give a specific example: Lets say you train two pairs of networks: (1) A Mamba SSM and a Transformer on the Pile. (2) Two transformers, one trained on the Pile, the other trained on Reddit comments. All are trained to the same MMLU performance.

I'd put big money that the average responses you get when sampling from the models in (1) are nearly identical, whereas the two models in (2) will be quite different.

There's not many people who will proudly announce their employer, their name, and where someone can stick it over the course of two public comments these days.

You, sir, are my hero.

If Mamba really was as capable as a Transformer on tasks requiring accurate attending to long context, then there'd be no need for Jamba (Mamba+Transformer hybrid).

Your argument of "if we train a Mamba SSM to be as good as a Transformer, then it'll be as good as a Transformer", seems a tad circular...

Please humor me for a moment, because I'm having trouble seeing why this is not just true by definition. Doesn't "training to the same performance" mean that you get the same responses? Or from a different angle: given that the goal of the model is to generate plausible completions based on a training dataset, it seems like plausibility (and therefore performance) is obviously defined by the dataset.

Yeah, I'm not sure how someone could interpret what you said in the way people are citing here. It's actually obvious that you are right in the context of data in LLMs. Look at LLAMA 3, for example there are minimal architectural changes, and its performance is almost at the level of GPT-4. The biggest change was in the dataset.

Well, both can be true if you interpret the "it" as "the secret sauce / competitive advantage". A good architecture is a necessary but not sufficient condition for success, but everybody uses more or less the same currently, so data makes the difference. Until the next improvement in architecture.

Or until we run out of data that actually differentiates the models

I do argue that the IT is the architecture. We have pretty much had all the data that these LLMs were trained on for a long time. The game changer was the architecture not the data. Unless of course you are on the code is data camp ;).

Probably the "it" is whatever one model has that other models don't have. When everyone is using the same architecture, then the data makes the difference. If everyone has the same data, then the architecture makes the difference.

It sounds pretty obvious to say that the difference is whatever is different, but isn't that literally what both sides of this argument are saying?

edit: I do think that what the original linked essay is saying is slightly subtler than that, which is that _given_ that everyone is using the same transformer architecture, the exact hyperparameters and fine tuning that is done matters a lot less than the data set does.

MLP is a universal approximator, so there’s definitely a configuration that can match an attention mechanism. Whether or not it’d be feasible to train is another question.

Not sure about feasible, but certainly not efficient.

I think this MLP universal approximator notion is similar to a Turing machine being a universal computation device. Correct, but practically useless.

I don't think Sutton's bitter lesson is going to result in everything being an MLP. You want the most scalable architecture, which an MLP certainly is not.

Yes, and note that in terms of different architectures, the author (James Betker) is talking about image generators, while when he's talking about LLMs they are all the same basic architecture - transformers.

Some tasks are going to be easier to learn that others, and certainly in general you can have more than one architecture capable of learning a given task, as long as it is sufficiently powerful (combination of architecture + size), and well trained.

That said, it's notable that all the Pareto optimal LLMs are transformer-based, and that in the 7 years since the attention paper (2017), all we have seen in terms of architectural change have been scaling up or minor tweaks like MoE and different types of attention.

How do you make a different architecture such as Mamba more competitive with transformers? Add some transformer layers to it (Jamba) !

So, yeah, as far as LLMs go, the precise model doesn't matter as long as it's a transformer, which isn't very surprising given what we know about how they work - primarily via induction heads. The lesson here isn't that architecture doesn't matter for LLMs, but rather that the architecture has to be a transformer! Data then becomes paramount, because the model learns the program (induction heads, etc) that runs on the machine (transformer) from the data.

No doubt there will be architectural advances beyond transformers, although few people seem to be currently looking for them, but I'm pretty sure they will still need something equivalent to the transformer's attention mechanism.

Seems like an objection that is slightly beside the point? The claim is not that literally any model gives the same result as a large transformer model, that's obviously false. I think the more generous interpretation of the claim is that the model architecture is relatively unimportant as long as the model is fundamentally capable of representing the functions you need it to represent in order to fit the data.

OP's claim/observation is that "trained on the same dataset for long enough, pretty much every model with enough weights and training time converges to the same point [of inference performance]".

His conclusion is that "It implies that model behavior is not determined by architecture, hyperparameters, or optimizer choices. It’s determined by your dataset, nothing else".

There is an implicit assumption here that seems obviously false - that this "convergence point" of predictive performance represents the best that can be done with the data, which is to imply that these current models are perfectly modelling the generative process - the human brain.

This seems highly unlikely. If they are perfectly modelling the human brain, then why do they fail so badly at so many tasks? Just lack of training data?

Interesting point. But, does the data contain enough information to perfectly model the generative process? Maybe even a very complex and capable model like "the human brain" would fail to model the datset better than large transformers, if that was the only thing they ever saw.

You and me can model the dataset better, but we're already "pre-trained" on reality for decades.

Just because the dataset is large doesn't mean it contains useful information.

Perhaps, but even with an arbitrarily good training set, the LLM would still be constrained by it's own architectural limits. e.g. If a problem can't be broken down into sub-problems that each require <= N sequential steps, then an N-layer transformer will never be able to solve it.

Even if the architectural shortcomings were all fixed, it seems "[pre-training] data is all you need" would still be false, because there is no getting around the need for personal experience, for the same reasons that is true for us...

Perhaps most fundamentally, any action/prediction you make can only based on the content of your own mind, not the mind of a tutor you are trying to copy. Even if the tutor diligently tries to communicate all nuances and contingencies of a skill to you, those are still all relative to his/her own internal world model, not the one in your head. You will need to practice and correct to adapt the instructions to yourself.

Machine learning insights from e e cummings.

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