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Caveman: Why use many token when few token do trick

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Re: Caveman: Why use many token when few token do trick

#131

Post by skydhash »

cyanydeez wrote: post #44
DrewADesign wrote: post #36

Earlier quoted context omitted.

> much of the thinking happens in a much higher dimensional space that just happens to be decoded as text. What do you mean by that? It’s literally text prediction, isn’t it?

There was a paper recently that demonstrated that you can input different human languages and the middle layers of the model end up operating on the same probabilistic vectors. It's just the encoding/decoding layers that appear to do the language management. So the conclusion was that these middle layers have their own language and it's converting the text into this language and this decoding it. It explains why some…

Pretty obvious when you think that neural networks operate with numbers and very complex formulas (by combining several simple formulas with various weights). You can map a lot of things to number (words, colors, music notes,…) but that does not means the NN is going to provide useful results.

Re: Caveman: Why use many token when few token do trick

#133

Post by andy99 »

TeMPOraL wrote: post #5

Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking . I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you ca…

I’ve heard this, I don’t automatically believe it nor do I understand why it would need to be true, I’m still caught on the old fashioned idea that the only “thinking” for autoregressive modes happens during training.

But I assume this has been studied? Can anyone point to papers that show it? I’d particularly like to know what the curves look like, it’s clearly not linear, so if you cut out 75% or tokens what do you expect to lose?

I do imagine there is not a lot of caveman speak in the training data so results may be worse because they don’t fit the same patterns that have been reinforcement learned in.

Re: Caveman: Why use many token when few token do trick

#134

Post by xgulfie »

TeMPOraL wrote: post #5

Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking . I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you ca…

Ah so obviously making the LLM repeat itself three times for every response it will get smarter

Re: Caveman: Why use many token when few token do trick

#135

Post by gchamonlive »

DonHopkins wrote: post #110
jerf wrote: post #73

Earlier quoted context omitted.

There is a study that shows that what the model is doing behind the scenes in those cases is a lot more than just outputting those tokens. For an LLM, tokens are thought. They have no ability to think, by whatever definition of that word you like, without outputting something . The token only represents a tiny fraction of the internal state changes made when a token is output. Clearly there is an optimal for each tas…

High dimensional vectors are thought (insofar as you can define what that even means). Tokens are one dimensional input that navigates the thought, and output that renders the thought. The "thinking" takes place in the high dimension space, not the one dimensional stream of tokens.

But isn't the one dimensional tokens a reflex of high dimensional space? What you see is "sure let's take a look at that" but behind the curtains it's actually an indication that it's searching a very specific latent space which might be radically different if those tokens didn't exist. Or not. In any case, you can't just make that claim and isolate those two processes. They might be totally unrelated but they also might be tightly interconnected.

Re: Caveman: Why use many token when few token do trick

#136

Post by rafram »

otabdeveloper4 wrote: post #69
TeMPOraL wrote: post #5

Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking . I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you ca…

LLMs don't think at all. Forcing it to be concise doesn't work because it wasn't trained on token strings that short.

They’re able to solve complex, unstructured problems independently. They can express themselves in every major human language fluently. Sure, they don’t actually have a brain like we do, but they emulate it pretty well. What’s your definition of thinking?

Re: Caveman: Why use many token when few token do trick

#137

Post by samusiam »

jstummbillig wrote: post #50
TeMPOraL wrote: post #5

Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking . I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you ca…

What do you mean? The page explicitly states: > cutting ~75% of tokens while keeping full technical accuracy. I have no clue if this claim holds, but alas, just pretending they did not address the obvious criticism, while they did, is at the very least pretty lazy. An explanation that explains nothing is not very interesting.

> I have no clue if this claim holds, but alas, just pretending they did not address the obvious criticism, while they did, is at the very least pretty lazy.

But they didn't address the criticism. "cutting ~75% of tokens while keeping full technical accuracy" is an empirical claim for which no evidence was provided.

Re: Caveman: Why use many token when few token do trick

#138

Post by skydhash »

mylifeandtimes wrote: post #80
suddenlybananas wrote: post #67

Earlier quoted context omitted.

>Some languages are far more expressive and specialized in logical conditions, conditionals, recursion and reasoning. Like eskimos have 100 words for snow, but for boolean algebra. This is simply not true.

Really? Because if one accepts that computer languages are languages, then it seems that we could identify one or two that are highly specialized in logical conditions etc. Prolog springs to mind.

We have already proven that all the computing mechanism that those languages derive their semantic forms are equivalent to the Turing Machine. So C and Prolog are only different in terms of notations, not in terms of result.

Re: Caveman: Why use many token when few token do trick

#139

Post by marginalia_nu »

TeMPOraL wrote: post #5

Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking . I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you ca…

I wonder if a language like Latin would be useful.

It's a significantly much succinct semantic encoding than English while being able to express all the same concepts, since it encodes a lot of glue words into the grammar of the language, and conventionally lets you drop many pronouns.

e.g.

"I would have walked home, but it seemed like it was going to rain" (14 words) -> "Domum ambulavissem, sed pluiturum esse videbatur" (6 words).

Re: Caveman: Why use many token when few token do trick

#140

Post by [deleted] »

TeMPOraL wrote: post #5

Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking . I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you ca…

[deleted]
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