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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

#141

Post by int_19h »

mdp2021 wrote: post #140
int_19h wrote: post #136

Earlier quoted context omitted.

Quite the opposite - I'm questioning fairness of judgment based on output from GPT-4 when solving complicated tasks etc, which is very hard to justify as "stochastic" unless you beg the question.

But the judgement is not relevant to a context of "«handwaving»" (BTW: nice concept). If you look at the "handwaving" it becomes a strawman to lose the focus on the objective. If an engine outputs things like "Charles III is the current King of Britain. He was born in January 26, 1763" , it seems to be conflating different notions without having used the necessary logic required for vetting the statement: the witness…

That is a completely different issue, though. We were not talking about the usefulness of the models' behavior, but rather of its fundamental nature. Right now I wouldn't trust an LLM for anything where the result is not either for entertainment purposes or undergoes human vetting. But I also don't trust many humans, and for reasons that are fundamentally the same - you implicitly acknowledged it by talking about "well-developed human intellect". And I would still trust a random human more than GPT-4 - but, again, this has more to do with its ability to reason being inherently limited (by model size, context size etc), not because it's originally trained to predict tokens.

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

#142

Post by narrator »

sebzim4500 wrote: post #93
napier wrote: post #88

Earlier quoted context omitted.

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.

That's some sci-fi stuff right there. A model so bad that even your researchers aren't allowed to talk to it.

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

#143

Post by kubanczyk »

alchemist1e9 wrote: post #100
flangola7 wrote: post #89

Earlier quoted context omitted.

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 pathoge…

This convinces me:

> Intelligence is the only advantage we have over lions, who are otherwise much bigger and stronger and faster than we are. But we have total control over lions, keeping them in zoos [...]

The "intelligence" is a mouthful, but I think the advantage described above is, at its root, caused by a slightly better "predict" step in the observe-predict-act loop. That's it. And LLMs aren't exactly bad predictors, are they? And it looks like while our predictive abilities rise over centuries, theirs rise over decades.

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

#144

Post by cubefox »

afro88 wrote: post #135
cubefox wrote: post #80

Earlier quoted context omitted.

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.

code-davinci-002 is the model used for code completions. Here's the full list of models: https://learn.microsoft.com/en-us/azure/cognitive-services/o...

Yeah, it can be also used for that, but the Azure characterization is misleading. The fact is that code-davinci-002 is the GPT-3.5 foundation model, the naked token predictor without any fine-tuning applied to it. See

https://platform.openai.com/docs/model-index-for-researchers

This makes it a very powerful tool in general, since it doesn't suffer from mode collapse. It can be used for anything, though one has to prompt it right, since it doesn't follow instructions by default.

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

#145

Post by nl »

gensym wrote: post #131
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 You really think Timnit Gebru and Margaret Mitchell, both of whom are cited in the first paper you footnoted, are ignorant of in-context learning?

Well the Gebru & Mitchell paper[1] was published before In-Context-Learning was discovered (an ICL was very unexpected), so yes, I think they were ignorant of ICL at the time.

Also their paper ("On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?") doesn't really define what they mean by a stochastic parrot, but they appear to mean mostly that it is non-grounded (section 6):

> Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that.

I think it is becoming increasingly clear that LLMs do in-fact have a "model of the world". Indeed even old techniques like Word2Vec (2014) showed that RNNs could build a model with meaningful relationships.

I don't think this paper really addresses that, although many of their other criticisms remain valid.

[1] https://dl.acm.org/doi/10.1145/3442188.3445922

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

#146

Post by anon291 »

nl wrote: post #23
anon291 wrote: post #19

Earlier quoted context omitted.

Except it cannot conceptualize reasonably. It is utterly incapable of symbolic thinking in a general sense.

No reason why Toolformer+GPT couldn't use a symbolic reasoning program. Symbolic reasoning programs are much better than humans. Probably bugs will happen in the interface between human language and the symbolic reasoning. But that happens with humans too.

I work in this field and have tried many things. Sure.. GPT + something else can do symbolic reasoning almost on par with a middle schooler (it gets things wrongs often, not in the way people do either, but just confidently incorrect). However, GPT + something is not a transformer model, which is what the original comment was about.

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

#147

Post by gensym »

nl wrote: post #145
gensym wrote: post #131

Earlier quoted context omitted.

> BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this You really think Timnit Gebru and Margaret Mitchell, both of whom are cited in the first paper you footnoted, are ignorant of in-context learning?

Well the Gebru & Mitchell paper[1] was published before In-Context-Learning was discovered (an ICL was very unexpected), so yes, I think they were ignorant of ICL at the time. Also their paper ("On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?") doesn't really define what they mean by a stochastic parrot, but they appear to mean mostly that it is non-grounded (section 6): > Text generated by an L…

> Well the Gebru & Mitchell paper[1] was published before In-Context-Learning was discovered (an ICL was very unexpected), so yes, I think they were ignorant of ICL at the time.

??? One of us is confused here (I'm fully willing to admit that it's me), but AFAIK, the big discovery of in-context learning was described in "Language Models are Few-Shot Learners" - published in 2020[1], vs 2021 for the stochastic parrots paper.

Regardless, it's not like the authors of the stochastic parrots paper have disavowed the term. They're still referring to the paper without correction in the statement they published about the proposed pause last month: https://www.dair-institute.org/blog/letter-statement-March20...

[1] https://arxiv.org/pdf/2005.14165.pdf

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