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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

#91

Post by avereveard »

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…

It's a hard problem that token space only partially solve, because with filling the token space to the brim with documents will dilute the instructions in the prompt.

One may argue that we'd need a separate channel for the instructions, but it would make harder to do anything but zero shot question and answer.

I think it's likely that systems with metadata for tagging sources, tasks separately from the human and ai interaction will eventually emerge. The gptchat api already works somewhat that way, but it isn't following the structure that we'll, probably because it was a later tune and not something built in the model from the beginning.

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

#92

Post by DebtDeflation »

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…

>if you wanted to say load all of a companies documents and ask questions

This is literally the use case that every large enterprise wants.

Why? The potential cost savings are enormous. A significant percentage of white collar jobs primarily involve performing repetitive tasks and answering frequently asked questions. If you could automate even 20% of this........

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

#93

Post by sebzim4500 »

napier wrote: post #88
afro88 wrote: post #74

Earlier quoted context omitted.

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.

Is it possible to get access to the base model (ie no RLHF) via Azure? Or even the version that’s part of Goodbing?

No, it is my understanding that even MS research employees do not have access to that anymore.

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

#94

Post by willbudd »

geraneum wrote: post #90
willbudd wrote: post #71

Earlier quoted context omitted.

> 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 th…

> Because you say so? Chill my friend, no need to get personal. We are talking about ideas. It’s OK to disagree. I am simply dismissing your initial claim. This usually happens when you present a scientific argument based on personal beliefs. If it’s not magic, then we should be able to doubt and examine it and it should eventually pass scientific muster. > No grounding is required… It evidently managed to learn a pr…

Wasn't intended to be personal. Just a mediocre way of expressing that your assertion there is missing any form of argumentation, and therefore as baseless as it is unconvincing.

I'm seeing an emergent capability of encoding higher order logic, and the whole point of such abstractions is to not need to hardcode your weights with the minutiae of cats on Mars. LLMs today are only trained to predict text, so it's hardly surprising that they have some gaps in their understanding of Newtonian physics. But that doesn't mean the innate capability of grasping such logic isn't there, waiting for the right training regime to expose it to its own falling apples, so to speak.

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

#95

Post by numeri »

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…

This paper is honestly fairly weak, if you look at it closely. The theoretical justifications rely heavily on approximating attention with linear attention, and really just show that linear attention could be written as (A + A_delta)x, then try to imply (not show – they have now theoretical justification for this next step) that A_delta is a gradient. One could make the same implication about almost any matrix-vector multiplication, by splitting the matrix into two pieces.

The empirical studies are fairly weak as well – none of them suggest that there is actually any gradient descent occurring during in-context learning, only that if you fine-tune a model's attention parameters (and no other parameters) for a set of examples, its internal representations and attention patterns are slightly more similar (and it's really not by much!) to those of in-context learning than before fine-tuning. Is it really that surprising? You could most likely take any two techniques that optimize a model towards some target, artificial restrict both of them to only apply to the same set of parameters, use both, and find increased similarity of the two optimized models compared to the non-optimized model.

None of that would be what the paper claims, i.e., a shared mechanism of gradient descent, just a similarity in outcome – and we all already knew that ICL and fine-tuning have similar results!

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

#96

Post by knome »

tgv wrote: post #85
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…

The "stochastic parrot" primarily serves to distinguish the (murky) workings of the human brain and that of an ANN. Increasing context doesn't change that. It might make some tasks feasible that cannot be performed now (although I can't see how to properly train it, but who knows), but it doesn't change the fact that the system as whole has a memory that works differently, and e.g. even doesn't monitor its own output…

>e.g. even doesn't monitor its own output vs. its intentions

It wouldn't have "intentions" in any human manner. It would have to project a response for each token, and then use that token to generate the next token of response. Almost like it has to rethink things through for each syllable.

One might compare it to giving a human a prompt and letting them add one word, and then giving the same prompt with that word added to a second human and letting them add one word, and so on. It could easily end up with a sensible end-state even though there was no continuous intent from start to end, just aligned intent token by token taken from context.

This is the most interesting response I've gotten that demonstrates gpt-4 viewing its own output as it is built up:

>Yes, I am familiar with the story you're referring to. The title of the short story is "The Machine That Won the War." It was written by Isaac Asimov and first published in 1961. The story is a conversation between three men who played major roles in a war against an alien race, and they discuss the role of a machine called Multivac in winning the war.

>However, it seems that I've made an error in my recollection, as the specific detail you mentioned—refusing to work until the engineer says 'please'—is from a different short story, "Sally," also by Isaac Asimov. In "Sally," autonomous cars stop working until a command is given courteously, using the word 'please.'

This was one large reply, not two separate ones.

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

#97

Post by wokkel »

willbudd wrote: post #53
nl wrote: post #31

Earlier quoted context omitted.

"Conditioned" with the ability to introduce new facts and have the model infer impacts on them. As an example, tell chatGPT that the Queen of England died (which occurred after the data cut off) and then ask it who the head of state of Australia is. It's able to infer the head of state of Australia is now Charles III (and gives a good explanation of how this is mostly ceremonial.) See https://twitter.com/nlothian/sta…

That's a brilliant example. Thanks for sharing. It demonstrates in a very straightforward way that LLMs are capable of learning (and applying) relationships at the level of abstraction of (at least) 1st order logic. It implies that during training, it learned the facts that Elizabeth is queen of the UK, and that Charles is its crown prince; but _also_ the logical rule transform(heir_to_the_throne, monarch) AND transf…

No it does not: if you google this and restrict the time to before 2021 (the learning cutoff date) you will find the same answer. Without having access to the training data it's impossible to tell what we seeing.

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

#98

Post by willbudd »

wokkel wrote: post #97
willbudd wrote: post #53

Earlier quoted context omitted.

That's a brilliant example. Thanks for sharing. It demonstrates in a very straightforward way that LLMs are capable of learning (and applying) relationships at the level of abstraction of (at least) 1st order logic. It implies that during training, it learned the facts that Elizabeth is queen of the UK, and that Charles is its crown prince; but _also_ the logical rule transform(heir_to_the_throne, monarch) AND transf…

No it does not: if you google this and restrict the time to before 2021 (the learning cutoff date) you will find the same answer. Without having access to the training data it's impossible to tell what we seeing.

> No it does not: if you google this and restrict the time to before 2021 (the learning cutoff date) you will find the same answer.

Not entirely sure what you mean, but ...show me? Why not just share a link instead of making empty assertions?

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

#99

Post by chimprich »

nl wrote: post #22
alchemist1e9 wrote: post #12

Earlier quoted context omitted.

Do we yet have a single example of a transformer based AI, i.e. LLM, learning something new that we didn’t teach it in the training data? Maybe we do. I’m not sure how to define it, but new should be a discovery or insight or even relationship that is not explicitly taught in the training data. If we don’t then it suggests without humans in the loop that the super intelligence is not so close?

There are plenty of examples of "new" things a LLM can do. A good example is all those toy examples of "Program a whatever in the style of Shakespeare and David Bowie's love child". This isn't a thing that it has seen in training data.

I don't think that's really "new". That's combining two existing styles that the LLM has seen in its training data, and the creative idea to combine those styles has been supplied by the operating human.

It's phenomenally impressive, and may be a stepping stone to models that can come up with new ideas, but I don't think we're there yet.

LLMs seem to be able to capture the idea of Shakespeare and Bowie's styles, and intuit a combination of the two, but when I start asking it questions about what it thinks about the process I don't get the impression of any understanding. It can magic up text from prompts but it doesn't understand what it's doing.

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

#100

Post by alchemist1e9 »

flangola7 wrote: post #89
alchemist1e9 wrote: post #17

Earlier quoted context omitted.

> I’m just saying, it was more a hopeful nod in the direction of wishful thinking. I still don’t understand why this is even wishful thinking, I don’t understand what the point of delaying progress is.

Because AI development is the most dangerous thing humans have ever done? Respectfully, have you been under a rock? "Progress" doesn't mean "every massive change to the world and humanity is good." There are undeveloped technologies that we are currently not capable of being responsible with. Google differential technological research.

I’m using GPT-4 basically everyday multiple times. I’m following closely LLM developments.

Yet I have trouble seeing what people find so dangerous. It’s amazingly cool stuff that will create massive productivity gains.

I guess I just lack needed imagination to believe this “most dangerous thing humans have ever done” perspective.

It looks to me the most dangerous thing is actually Gain Of Function research on pathogens. That seems like most dangerous thing. Close 2nd is nuclear weapons.

LLMs seems really nice and fuzzy and warm to me in comparison.

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