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How LLMs work

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Re: How LLMs work

#241

Post by stevenhuang »

Borealid wrote: post #220
layla5alive wrote: post #203

Earlier quoted context omitted.

You can also teach humans science and math and then they can be trained by a cult to not use any of that reasoning when emitting canned responses that they were rewarded by the cult for internalizing during their training. "Fake News!" You're caught up on the mechanics of token processing (floating point matrix ALU math) and ignoring the context that p(next token) as a function being "computed" is doing so over a tri…

I completely agree that humans sometimes are not applying reasoning to things. I'm not trying to argue a model can not "reason" or have "cognition", whatever those things are. I'm only saying that it's absolutely the case that whatever those things are, they come from its mechanism of predicting one token at a time ad infinitum, and that throwing away a deep understanding in favor of a shallow one is foolish. Just be…

A dominant theory for human cognition is predictive coding

https://en.wikipedia.org/wiki/Predictive_coding

Re: How LLMs work

#242

Post by stevenhuang »

perching_aix wrote: post #239
otabdeveloper4 wrote: post #233

Earlier quoted context omitted.

LLMs predict next token one at a time. (Stochastically.) Literally. It's what they do. That's how they literally work. If you don't believe me, download llama.cpp and see for yourself. P.S. I write inference backends in C++ every day. The gall of people like you who figured out how to prompt Claude and think they're hot shit now is simply unbelievable.

So you work on inference engines, and don't see at all what'd be hilariously disingenuous and reductive about describing how LLMs operate as "just parroting the most statistically likely next token"? It is literally* what they do, yes. And only literally, with a big asterisk of "non-colloquial meaning" after the word "statistically". Like how "significant" means something pretty different, albeit related, in academic…

Look at his comments here.

Safe to say there's a cognitive block and until he tries to approach this topic in good faith he'll simply never understand. Lol.

https://news.ycombinator.com/item?id=48429027

Re: How LLMs work

#243

Post by [deleted] »

stevenhuang wrote: post #242
perching_aix wrote: post #239

Earlier quoted context omitted.

So you work on inference engines, and don't see at all what'd be hilariously disingenuous and reductive about describing how LLMs operate as "just parroting the most statistically likely next token"? It is literally* what they do, yes. And only literally, with a big asterisk of "non-colloquial meaning" after the word "statistically". Like how "significant" means something pretty different, albeit related, in academic…

Look at his comments here. Safe to say there's a cognitive block and until he tries to approach this topic in good faith he'll simply never understand. Lol. https://news.ycombinator.com/item?id=48429027

[deleted]

Re: How LLMs work

#244

Post by perching_aix »

stevenhuang wrote: post #242
perching_aix wrote: post #239

Earlier quoted context omitted.

So you work on inference engines, and don't see at all what'd be hilariously disingenuous and reductive about describing how LLMs operate as "just parroting the most statistically likely next token"? It is literally* what they do, yes. And only literally, with a big asterisk of "non-colloquial meaning" after the word "statistically". Like how "significant" means something pretty different, albeit related, in academic…

Look at his comments here. Safe to say there's a cognitive block and until he tries to approach this topic in good faith he'll simply never understand. Lol. https://news.ycombinator.com/item?id=48429027

It's so beyond tiresome. It's a classic case of someone being technically correct, and abusing the gap between that, and what people actually gather from it, for sentiment manipulation (willfully or otherwise). And I have a pretty hard time believing at this point that it's the otherwise.

I really don't know what's so interesting about auto-complete or next token prediction that it captures these people's attention so much. They're so blatantly not the salient quality to these products that is of interest to the common discourse, it's just baffling.

Re: How LLMs work

#245

Post by timmytokyo »

mattnewton wrote: post #28
zemo wrote: post #21

Earlier quoted context omitted.

normal people talk and write with some notion of meter, the cadence of communicating where pauses are inserted at places that naturally suit the speaker (and listener) to pause for thought. LLM's don't really do that, they just write a bunch of sentences. > Researchers have found that some neurons inside the FFN are strongly associated with specific concepts or facts. One neuron might activate strongly on Eiffel-Towe…

I don’t disagree with your conclusion that this is likely ai rewritten, but I do find it strange that you say “normal people don’t write like this” when it is mimicking how people write, and using patterns I have seen people write. I think models are at the point where style is not really reliable as an indicator anymore.

LLMs average out all the writing they were trained on. Individuality and idiosyncrasy are flattened out or removed. That's why it all reads the same.

Re: How LLMs work

#246

Post by aplomb1026 »

bluerooibos wrote: post #142
2muchcoffeeman wrote: post #110

Earlier quoted context omitted.

I think you need to ask what you actually want to do with the AI. If you want to be a researcher and come out with the next breakthrough, get ready to go back to school and learn some math. If you just need to learn how to use it well and build things with it, then you probably just need to have a high level understanding. Same as programming. I’d bet most programmers have no idea about the physics that makes compute…

> I think you need to ask what you actually want to do with the AI. What about improving the efficiency of token consumption, etc., basically opportunities for improving cost/performance? I keep thinking there has to be a better way to share context with models than dumping entire gigantic skill files of raw text or otherwise into them - I'm betting there's a bunch of low-hanging fruit there.

[flagged]

Re: How LLMs work

#247

Post by avazhi »

vocram wrote: post #68

Saying an article is of inferior quality just because editing was AI-assisted is like saying a book is lower quality just because it was printed rather than written by hand

This article was not A.I.-assisted, it was clearly completely written by an AI in places.

Re: How LLMs work

#248

Post by hackinthebochs »

Borealid wrote: post #240
hackinthebochs wrote: post #155

Earlier quoted context omitted.

>At all times the LLM is, indeed, predicting the next token The point is that saying they're just "predicting the next token" is not at all explanatory nor providing insight. Saying the brain is just firing action potentials gives you no understanding about how the brain does what it does or what the space of its capabilities are. Similarly, predicting the next token tells you nothing about the capabilities of LLMs.

The point is that the output is text that is statistically correlated with the input. The capability of the LLM is not to reason, it's to generate text that matches the patterns seen in the training corpus. It's possible that all you need to "reason" is plausible text generation. I'm not saying it's not. But nothing the LLM does fails to be explained by plausible-text-generation. I contend that the best way to unders…

>The point is that the output is text that is statistically correlated with the input.

But we can simply note that this description applies to any machine learning algorithm. Yet LLMs are lightyears better than, say, Markov chains. What people are after is something that elucidates the features of LLMs that allow them to be so productive over what came before.

Re: How LLMs work

#249

Post by layla5alive »

otabdeveloper4 wrote: post #231
layla5alive wrote: post #221

Earlier quoted context omitted.

decode() looks simple! Wow, obviously intelligence can't live behind that function call! /s Now, take that for loop, and replace the implementation of decode(context, ++position) and pass it to a human who was bored enough to play along and use a notebook to organize their thoughts and translate them to/from this encoding (you might write a helper function to do this for the human in the front-end of the new decode()…

The LLM predicts next token one at a time. (Stochastically.) This is a literal truth. Deal with it.

It's a literal truth that predicting the next token one at a time does not preclude intelligence on the other side of the decode function. Deal with it.

Re: How LLMs work

#250

Post by cavemandaveman »

jochembrouwer wrote: post #234
ekunazanu wrote: post #76

Earlier quoted context omitted.

> This was openai’s entire breakthrough. Making this particular model architecture larger leads to emergent capabilities Basically, the bitter lesson: https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson...

So the take-away here is that we (as humans) try to model these AIs like humans, but eventually these AIs get better. Which to me seems like a logical conclusion if they can do "things" (like "learning" or pattern matching) much faster than we can (the compute). Then language in LLMs is a bottleneck, the AI is constrained by the language, and thus if we want to scale further we could let AI create its own language (w…

My understanding is that the Inuit snow claim is a bit of a myth. Beyond English words like slush, sleet, powder, hardpack, flurry, blizzard, etc you can also say "fluffy snow", "wet snow", etc. The Inuit language is just smooshing the adjective so you get something like "wetsnow" as one word.
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