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How LLMs work

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Re: How LLMs work

#81

Post by janalsncm »

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

Not just that, I think a lot of people are going to waste their time losing the battle (and make no mistake, they will lose) fighting against AI writing without ever asking themselves what makes writing good in the first place.

There’s good AI writing and bad organic writing. But it’s easier to point out a few LLM-isms than to actually identify the problems with text.

Re: How LLMs work

#82

Post by uoaei »

rfv6723 wrote: post #62
ctolsen wrote: post #38

Earlier quoted context omitted.

No, it’s definitely not what a human brain is. That makes very little sense. The ways we interact with language (and thus conceptual memory) is completely and fundamentally different.

Is it different though? If we look beyond written languages which are late inventions of human civilization, oral languages are continuous and build with blocks not words. Chomskyan school misled the entire field of linguistics for decades by ignoring spoken languages.

It is different, but there may be some universal principles that are relevant more abstractly among both cases. Of particular interest is the empirical notion that statistical models of a certain form will always tend to "average out noise" and "learn meaningful patterns" up to the capacity that those models have for representing said patterns. A parallel notion to this is the hypothesis dubbed "thermodynamic origins of life". The universal principle binding these two seemingly disparate topics is one that seems to underlie any sense of "learning" in physical systems: that semantics of those systems depend on their representational power, and the semantics they do come to represent are the results of adding up many pushes in one "direction" (phase space / state space / etc.) encoding a pattern, and adding up many random noise jiggles will cancel out but give you a first-order sense of variance of those semantic features as expressed by the environment.

As this description is so overly abstract, an exercise for the reader is to try to work through an explanation of how, say, a river delta comes to "learn" about its environment by "reacting" to the influences at its borders, and how it "encodes" whatever it is that it learns in the substrate that it inhabits.

Re: How LLMs work

#83

Post by Laurel1234 »

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

Rather interesting than clanker slop defenders downplay the clanker aspect and highlight the human by calling it "ai-assisted", which defeats their entire point.

I hope you do some introspection and start consciously recognizing that the human input and the clanker slop is just debasing it.

Re: How LLMs work

#84

Post by [deleted] »

10GBps wrote: post #15
malwrar wrote: post #10

Back when ChatGPT came out, I was so shocked by how _good_ it was for an “AI” product that I simply had to know how it worked. Over the next month I ended up drawing out a block diagram on a whiteboard I have in my office, with the math involved next to each step in the blackboard. I’d puzzle about each step along the way, and the triumph of completing the drawing was also that of this sense of deep understanding. I…

Yep. It's nearly identical to the neural nets we were using in the 90s. Back then even a supercomputer wasn't big enough or fast enough to do what we do today. I have to wonder though. Is this all a human brain is? A similar thing to an LLM just scaled exponentially larger. I mean a brain is not just neurons with simple connections to each other. The neurons, axons, dendrites, , etc in a brain are all holding and pro…

[deleted]

Re: How LLMs work

#85

Post by Gmolomo »

malwrar wrote: post #10

Back when ChatGPT came out, I was so shocked by how _good_ it was for an “AI” product that I simply had to know how it worked. Over the next month I ended up drawing out a block diagram on a whiteboard I have in my office, with the math involved next to each step in the blackboard. I’d puzzle about each step along the way, and the triumph of completing the drawing was also that of this sense of deep understanding. I…

Sooooo just because you are able to understand it, it's not worth anything?

It doesn't has any impact?

Ah wait it does. Mh weird.

Why are you not creating a startup and get rich?

Re: How LLMs work

#86

Post by crossroadsguy »

malwrar wrote: post #10

Back when ChatGPT came out, I was so shocked by how _good_ it was for an “AI” product that I simply had to know how it worked. Over the next month I ended up drawing out a block diagram on a whiteboard I have in my office, with the math involved next to each step in the blackboard. I’d puzzle about each step along the way, and the triumph of completing the drawing was also that of this sense of deep understanding. I…

What hopes/paths does a mere CS bachelor (not deep into stats/maths), and mid level dev (native mobile only; 10-15 years exp.), have about not only understanding it (maybe not fully) but getting possibly into this as a career? Not expecting churning out models and AI systems from the first weeks/months but entry/employment into this field?

(If I can be honest, and I am not being disparaging about anything lest it might seem so, I am looking at it from a career breakthrough/move perspective rather than an intellectual pursuit.)

Re: How LLMs work

#87

Post by helloplanets »

The part about positional encoding is not correct.

> The intuition: instead of adding position info to each token’s vector, RoPE rotates the vector by an angle that depends on its position

You can't rotate the token's entire vector (or all three vectors, whatever is being implied is unclear). You rotate each token's Query and Key vectors only, so dot product can be used to tell how far apart the tokens are when comparing token 1's Query vector to token 2's Key vector.

Positional embedding should just be explained after explaining the Query, Key and Value vectors. When the article explains those only after that, the reader is building up on a wrong intuition and it gets confusing.

Re: How LLMs work

#88

Post by AltruisticGapHN »

I don't like how most LLM explainer articles and videos say that essentially a LLM " predicts the next word".

I'm a developer but not very good at maths and I still don't understand any of it.

A LLM clearly has some "visual" capacity. You ask Gemini to build something with Canvas and it's able to reason about the shape of things. Like recently I waanted a checkbox that has like a gradient flowing around the edge. It figured out it could use a radial gradient from the center of the checkbox, and overlay that with a small inner div so you only see the edge that looks like the gradient is circling around the checkbox.

How is that "predicting the next word"?

Not saying AI is intelligent or conscious or anything like that, but the algorithm clearly is far more complex than "predicting words".

What I mean, is the LLM is able to represent things in space . That part I don't understand.

I also still dont understand the relationship between the chat based LLM and the multi modal stuff. I think I read somewhere when image is generated it is also tokens?

Re: How LLMs work

#89

Post by sarjann »

Gmolomo wrote: post #85
malwrar wrote: post #10

Back when ChatGPT came out, I was so shocked by how _good_ it was for an “AI” product that I simply had to know how it worked. Over the next month I ended up drawing out a block diagram on a whiteboard I have in my office, with the math involved next to each step in the blackboard. I’d puzzle about each step along the way, and the triumph of completing the drawing was also that of this sense of deep understanding. I…

Sooooo just because you are able to understand it, it's not worth anything? It doesn't has any impact? Ah wait it does. Mh weird. Why are you not creating a startup and get rich?

I mean there is a little something called compute. And other complexity that comes like writing code to efficiently distribute a model across machines.

Re: How LLMs work

#90

Post by Borealid »

AltruisticGapHN wrote: post #88

I don't like how most LLM explainer articles and videos say that essentially a LLM " predicts the next word". I'm a developer but not very good at maths and I still don't understand any of it. A LLM clearly has some "visual" capacity. You ask Gemini to build something with Canvas and it's able to reason about the shape of things. Like recently I waanted a checkbox that has like a gradient flowing around the edge. It…

Your casual understanding is imprecise.

At all times the LLM is, indeed, predicting the next token. Anything it does emerges from that.

It did not "figure anything out". It predicted that text describing the use of a radial gradient was likely to follow text describing your problem.

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