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Scaling Transformer to 1M tokens and beyond with RMT

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Re: Scaling Transformer to 1M tokens and beyond with RMT

#71

Post by willbudd »

geraneum wrote: post #69
willbudd wrote: post #56

Earlier quoted context omitted.

Au contraire. Learning an abstract logical relationship such as line of succession during training, and then applying substitution/reification during inference to deduce the new factual clause that Charles is king of the UK is exactly what it means to learn something new. It's just a pity it can't memorize this fact at inference time, and that won't be able to reproduce it as soon as the information about the queen's…

That’s actually correct but an overfitted definition for learning. It holds certain hidden assumptions (i.e physical grounding) of the learner being human which makes it inapplicable to an LLM. As in a self driving car which passes a driving exam but fails to drive effectively freely in the city (it’s not an LLM but relevant in this context). You have to admit when you work with this tech that something fundamental i…

> That’s actually correct but an overfitted definition for learning. It holds certain hidden assumptions (i.e physical grounding) of the learner being human which makes it inapplicable to an LLM.

Inapplicable why exactly? Because you say so? Logic isn't magic. Nor is learning. No (external) grounding is required either: iteratively eliminating inconsistent world models is all you need to converge toward a model of the real world. Nothing especially human or inhuman about it. LLM architecture may not be able to represent a fully recursive backtracking truth maintenance system, but it evidently managed to learn a pretty decent approximation anyway.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#72

Post by quickthrower2 »

kgwgk wrote: post #29
nl wrote: post #26

For those who don't completely get the impact of this: We already know Large Language Models (LLMs) can learn at runtime (ie, separately to the training process.) This is called "In Context Learning". See [1], [2] for more details. (BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this) In context learning is wonderful because it means you can "train" a LLM at run time by filling the…

> (BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this) Do you? Some of them -at least- understand that the output is conditional on the input.

Output conditional on input (training data included) where input is, well, fucking huge and the training cycles ridiculous!

Re: Scaling Transformer to 1M tokens and beyond with RMT

#73

Post by l33tman »

"Figure 2: Recurrent memory mechanism. Memory is passed to Transformer along input sequence embeddings, and memory output is passed to the next segment. During training gradients flow from the current segment through memory to the previous segment."

The whole point of the 2017 paper "Attention is all you need" was to show that the recurrent structure previously used (with attention mechanisms) wasn't needed. It's pretty cool if it does turn out that both mechanisms are needed and powerful together. After all, the brain doesn't linearly process the entire book you're reading from page 1 to where you are, for every new word you read, it stashes the important parts into short/long term memory along the way.

Still, passing gradients back through several stages like that is typically a tricky thing to do in practice.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#74

Post by afro88 »

capableweb wrote: post #68
nl wrote: post #26

For those who don't completely get the impact of this: We already know Large Language Models (LLMs) can learn at runtime (ie, separately to the training process.) This is called "In Context Learning". See [1], [2] for more details. (BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this) In context learning is wonderful because it means you can "train" a LLM at run time by filling the…

> GPT-4 recently extended that to 32,000 tokens Slightly unrelated, but has anyone outside of OpenAI got access to this model yet? While I received API access to GPT4 just a day or two after applying for it, I've yet to succeed to get access to the 32k version, nor has anyone I know, and I have not seen it being used by anyone in the wild either.

I have access to it through Azure.

First you need to get approved for OpenAI access via Azure Cognitive Services: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?...

Then you need to get approved for GPT4: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?...

Once approved for GPT4, you'll have access to the GPT4-32k model.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#75

Post by Piezoid »

nl wrote: post #26

For those who don't completely get the impact of this: We already know Large Language Models (LLMs) can learn at runtime (ie, separately to the training process.) This is called "In Context Learning". See [1], [2] for more details. (BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this) In context learning is wonderful because it means you can "train" a LLM at run time by filling the…

In neuroscience, predictive coding [1] is a theory that proposes the brain makes predictions about incoming sensory information and adjusts them based on any discrepancies between the predicted and actual sensory input. It involves simultaneous learning and inference, and there is some research [2] that suggests it is related to back-propagation.

Given that large language models perform some kind of implicit gradient descent during in-context learning, it raises the question of whether they are also doing some form of predictive coding. If so, could this provide insights on how to better leverage stochasticity in language models?

I'm not particularly knowledgeable in the area of probabilistic (variational) inference, I realize that attempting to draw connections to this topic might be a bit of a stretch.

[1] The free-energy principle: a unified brain theory: https://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20prin...>

[2] Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?: https://arxiv.org/abs/2202.09467

Re: Scaling Transformer to 1M tokens and beyond with RMT

#76

Post by mdp2021 »

nl wrote: post #26

For those who don't completely get the impact of this: We already know Large Language Models (LLMs) can learn at runtime (ie, separately to the training process.) This is called "In Context Learning". See [1], [2] for more details. (BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this) In context learning is wonderful because it means you can "train" a LLM at run time by filling the…

That is very interesting.

But - note - the label of "stochastic parrots" is not really relevant to the acquisition of notions and the appearance of some consistency checks, but more to the idea that the ideal system performs the checks regularly and deeply - especially in front of new ideas, of which the ones it outputs are especially relevant (they had been in just before, so a new part of the cognitive set, and you expect them rigorously examined). Whatever you utter, you have supposed to have thought about critically with some depth - structurally.

You acquire a notion, you criticize it, you produce new notions, you criticize them, you build on an epistemic corpus born on critical effort.

In fact, it is a gamechanger to see that "critical thinking" is possible with LLMs - but do not forget that we have seen many examples in months of that not actually happening.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#77

Post by cubefox »

nl wrote: post #41
geraneum wrote: post #37

Earlier quoted context omitted.

> In context learning is wonderful because it means you can "train" a LLM at run time The “learning” in “In context learning” is not the same concept as learning during the network “training”. The former is loosely named and does not have any impact on what’s actually “learned” by the model. The LLM can, by nature, contradict its training data and whatever you/it prompt(s), and incorporates randomness when completing…

> The “learning” in “In context learning” is not the same concept as learning during the network “training”. The former is loosely named and does not have any impact on what’s actually “learned” by the model. Actually (and very surprisingly!) it is related. See the "Similarity of Attention Output Updates" and "Similarity of Attention Map" metrics in https://arxiv.org/abs/2212.10559 (linked above) where they show that…

For LLMs there are at least three different ways of "learning":

- Pre-training (for text prediction, using unsupervised learning)

- Fine-tuning (e.g. to follow instructions or to reject certain queries, using supervised and/or reinforcement learning) (optional)

- in-context learning (using "few-shot prompts" as examples)

Now the last two can have similar effects. For example, you might fine-tune a foundation (only pre-trained) model to follow instructions, or you don't and instead just modify your prompt such that it looks like a dialogue between a human and a helpful chat assistant.

But neither can replace the extensive pre-training phase, which is what gives the model all its intelligence.

One other disanalogy between fine-tuning and in-context learning appears to be that the model can't exactly remember the data it was fine-tuned with, while it "knows" exactly everything in its context window. That is its working memory, so to speak.

Re: Scaling Transformer to 1M tokens and beyond with RMT

#78

Post by mdp2021 »

afro88 wrote: post #74
capableweb wrote: post #68

Earlier quoted context omitted.

> GPT-4 recently extended that to 32,000 tokens Slightly unrelated, but has anyone outside of OpenAI got access to this model yet? While I received API access to GPT4 just a day or two after applying for it, I've yet to succeed to get access to the 32k version, nor has anyone I know, and I have not seen it being used by anyone in the wild either.

I have access to it through Azure. First you need to get approved for OpenAI access via Azure Cognitive Services: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?... Then you need to get approved for GPT4: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?... Once approved for GPT4, you'll have access to the GPT4-32k model.

Do you have any remarks to share?

Re: Scaling Transformer to 1M tokens and beyond with RMT

#79

Post by youssefabdelm »

afro88 wrote: post #74
capableweb wrote: post #68

Earlier quoted context omitted.

> GPT-4 recently extended that to 32,000 tokens Slightly unrelated, but has anyone outside of OpenAI got access to this model yet? While I received API access to GPT4 just a day or two after applying for it, I've yet to succeed to get access to the 32k version, nor has anyone I know, and I have not seen it being used by anyone in the wild either.

I have access to it through Azure. First you need to get approved for OpenAI access via Azure Cognitive Services: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?... Then you need to get approved for GPT4: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?... Once approved for GPT4, you'll have access to the GPT4-32k model.

With image input capability?

Re: Scaling Transformer to 1M tokens and beyond with RMT

#80

Post by cubefox »

afro88 wrote: post #74
capableweb wrote: post #68

Earlier quoted context omitted.

> GPT-4 recently extended that to 32,000 tokens Slightly unrelated, but has anyone outside of OpenAI got access to this model yet? While I received API access to GPT4 just a day or two after applying for it, I've yet to succeed to get access to the 32k version, nor has anyone I know, and I have not seen it being used by anyone in the wild either.

I have access to it through Azure. First you need to get approved for OpenAI access via Azure Cognitive Services: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?... Then you need to get approved for GPT4: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?... Once approved for GPT4, you'll have access to the GPT4-32k model.

Are there other OpenAI models only available via Azure? I think code-davinci-002, the GPT-3.5 base model, is now only available via Azure. The GPT-4 base model seems to be completely unavailable. The OpenAI playground only has the old GPT-3 base model, davinci.
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