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Simply explained: How does GPT work?

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Re: Simply explained: How does GPT work?

#91

Post by photochemsyn »

nitnelave wrote: post #50
seydor wrote: post #17

This is confusing, using the semantic vectors arithmetic of embeddings is not very relevant to transformers and its completely missing the word 'attention'. I don't think transformers are that difficult to explain to people , but it is hard to explain "why" they work. But i think it's important for everyone to look under the hood and know that there are no demons underneath.

I was trying to keep the article at a level that everyone understands, from middle school up. I thought about going a bit deeper in the structure and mentioning attention, but my problem is that the intuitive concept of "attention" is quite different from the mathematical reality of an attention layer, and I'm sure I would have lost quite a few people there. It's always a trade-off :)

No worries, readers can just ask ChatGPT 'what's the meaning of attention in the context of LLM design' and go from there [edit also temperature].

Re: Simply explained: How does GPT work?

#92

Post by myrmidon »

agentultra wrote: post #84
myrmidon wrote: post #68

Earlier quoted context omitted.

> That's highly reductive of our capacities. I'm not saying that GPT4 is as capable as a human-- it can not be, by design, because its architecture lacks memory/feedback paths that we have. What I'm saying is that HOW it thinks might already be quite close in essence to how WE think. > We are not weighted transformers that can be explained in an arxiv paper. GPT, at the end of the day, is a statistical inference mode…

> What I'm saying is that HOW it thinks might already be quite close in essence to how WE think. How would one be able to prove this? Nobody knows how we think, yet. All one can say is that what GPT-4 outputs could plausible fool another human into believing another human wrote it. But that's exactly what it's designed to do, so what's interesting about that? > If you concede that our brain is "simulatable", It could…

> We're talking about writing an emulator on a Harvard-architecture computer that can fully simulate the physics and biological processes the make up a human brain. By interpreting this system in our emulator we'd be able to witness a new human being that is indistinguishable from one that isn't simulated, right?

My point is: if you don't believe that there is magic pixy dust in our brains, then this would NECESSARILY be possible.

It would almost certainly be HIGHLY inefficient-- the "right way" to do AGI would be to find out which algorithmic structures are necessary for human level "performance", and implement them in a way that is suitable for your VM.

I'm arguing that GPT4 is essentially the second approach-- it lacks features for full human level performance BY DESIGN (e.g. requires pre-training, no online learning, etc.), but there is no reason to assume that the way it operates is fundamentally different from how *parts* of OUR mind work.

> It turns out there's more to being human than being a register VM. Ever get punched in the face? Bleed? Fall in love? Look back on your life and decide you want to change? Write a book but never show it to anyone? Raise a child? Wonder why you dreamt about airplanes on Mars with your childhood imaginary friend? Why you hate bananas but like banana bread? Why you lie to everyone around you about how you really feel and are offended when others don't tell you the truth?

I don not understand what you are getting at here. I consider myself a biological machine-- none of this is inconsitent with my worldview. I believe that a silicon based machine could emulate all of this if wired up properly.

PS: I often talk with people that explicitly DONT believe into the "pixy dust in our brains" (call it soul if you want), but on the other hand they strongly doubt the feasibility of AGI-- this is internally inconsistent and simply not a defensible point of view IMO.

Re: Simply explained: How does GPT work?

#93

Post by HarHarVeryFunny »

gcr wrote: post #67
seydor wrote: post #36

Earlier quoted context omitted.

gptX embeddings aren't even words. Even so, the embedding relationship is useful but not the core of what transformers do to find relationships between words in sequences.

remember the word2vec paper? the surprising bit the authors were trying to show was that putting words in some embedding space with an appropriate loss naturally lends enough structure to those words to be able to draw robust, human-interpretable analogies. I agree with the sentiment that each individual dimension isn't meaningful, and I also feel like it's misleading for the article to frame it that way. But there's…

I wouldn't say the interpretability of word2vec embeddings is suprising - it's just a reflection of words being defined by context/usage, and these embeddings being created based on that assumption.

Re: Simply explained: How does GPT work?

#94

Post by charles_f »

I commend the author for one of the clearest explanations I've seen so far, written to explain rather than impress. Even an idiot like myself understood what is explained.

Two things that I felt were glanced over a bit too fast were the concept of embeddings and that equation and parameters thing. Consider elaborating a bit more or giving an example

Re: Simply explained: How does GPT work?

#95

Post by petra »

fastball wrote: post #85
seydor wrote: post #71

Earlier quoted context omitted.

Indeed the word 'attention' implies some active process, which is also unfortunate, because it is really similarity maps over long ranges. This process apparenty creates a cross-reference maps of items that are commonly co-activated. But then what do the different heads do? Some kind of different "modalities of cross-reference"? I struggle to understand why this thing works the way it does. It's possible that Vaswani…

Multi-head attention just means that you're looking at all the words at once rather than only looking at one word at a time, and using that to generate the next word. So instead of using attention only on the last word you also have attention on the penultimate word and the one before that and the one before that, etc. I think it is fairly obvious why this gives better results than say an RNN – you are utilizing cont…

So in training, chatgpt turned words into embedding , and given context window N , looked at N embeddings an created a probabilities list for the following next embedding ?

And if I tell it something that was excatly in it's trained context windows, I get the most likely next word and the one after itm

But what happens if I ask it something slighty different than it's training context ? Or something largely different?

Re: Simply explained: How does GPT work?

#96

Post by rimliu »

vadansky wrote: post #31
habosa wrote: post #20

Is it possible that we don’t truly know how it works? That there is some emergent behavior inside these models that we’ve created but not yet properly described? I’ve read a few of these articles but I’m still not completely satisfied.

I hate being the bearish guy during the hype cycle, but I think a lot of that is just anthropomorphizing it. They fed it TBs of human text, it spits out human text, we think it's humanesque. Of course maybe I'm wrong and it's AGI and it will find this comment and torture me for for insulting it's intelligence.

I really liked the analogy to a student given in this wonderful article: https://www.newyorker.com/tech/annals-of-technology/chatgpt-...

Basically, due to it's nature ChatGPT cannot repeat things verbatim, so it rephrases it. In humans we associate the ability to rephrase stuff with the understanding the material as opposed to rote learning, so we transfer the same concept over to ChatGPT and it suddenly appears "intelligent" despite having zero concepts of whatever stuff it spits out.

Re: Simply explained: How does GPT work?

#97

Post by rimliu »

Analog24 wrote: post #24
winternett wrote: post #21

Where is IBM's Watson in all this? It seems as if it never existed? That is just one example of how companies keep making these grand presentations and under-delivering on results... Plain and simple the over-hyped GPT editions are NOT truly AI, it is scripting to assemble coherent looking sentences backed by scripts that parse content off of of stored data and the open web into presented responses.... There is no "a…

What would be the differentiating factor(s) for true AI/intelligence in your opinion?

Having a world model

Re: Simply explained: How does GPT work?

#98

Post by heyitsguay »

fastball wrote: post #85
seydor wrote: post #71

Earlier quoted context omitted.

Indeed the word 'attention' implies some active process, which is also unfortunate, because it is really similarity maps over long ranges. This process apparenty creates a cross-reference maps of items that are commonly co-activated. But then what do the different heads do? Some kind of different "modalities of cross-reference"? I struggle to understand why this thing works the way it does. It's possible that Vaswani…

Multi-head attention just means that you're looking at all the words at once rather than only looking at one word at a time, and using that to generate the next word. So instead of using attention only on the last word you also have attention on the penultimate word and the one before that and the one before that, etc. I think it is fairly obvious why this gives better results than say an RNN – you are utilizing cont…

That's not what multi-head attention means. Multi-head attention is the use of learned projection operators to perform attention operations within multiple lower-dimensional subspaces of the network's embedding space, rather than a single attention operation in the full embedding space. E.g. projecting 10 512-D vectors into 80 64-D vectors, attending separately to the 8 sets of 10 embedding projections, then concatenating the results together to reform 10 512-D vector outputs.

In fact the projection operations are the only learned part of a Transformer's self-attention function -- the rest of self-attention is just a weighted sum of the input vectors, where the weights come from the (scaled) vector correlation matrix.

Re: Simply explained: How does GPT work?

#99

Post by [deleted] »

onetrickwolf wrote: post #7

I've been using GPT4 to code and these explanations are somewhat unsatisfactory. I have seen it seemingly come up with novel solutions in a way that I can't describe in any other way than it is thinking. It's really difficult for me to imagine how such a seemingly simple predictive algorithm could lead to such complex solutions. I'm not sure even the people building these models really grasp it either.

[deleted]

Re: Simply explained: How does GPT work?

#100

Post by stareatgoats »

rootusrootus wrote: post #51
stareatgoats wrote: post #10

This article seems credible and actually made me feel as if I understood it, i.e. at some depth but not deeper than a relative layperson can grasp. What I can't understand is how the Bing chatbot can give me accurate links to sources but chatGPT4 on request gives me nonsensical URLs in 4 case of 5. It doesn't matter in the cases where I ask it to write a program: the verification is in the running of it. But to have…

> It doesn't matter in the cases where I ask it to write a program: the verification is in the running of it. Even then. I've had it write programs that were syntactically correct and produced plausible, but incorrect behavior. I'm really careful about what I'll use GPT-generated code for. IMO write the tests yourself, at least.

Absolutely! It is seldom correct right off the bat.
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