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GPT-4 Understands (danangell.com)

submitted 1 year ago by [email protected] to c/[email protected]

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[–] [email protected] 2 points 1 year ago (17 children)

They are saying the internal vector space that LLMs use is too complicated and too unrelated to the output to be understandable to humans.

Yes, that's exactly what I'm saying.

That doesn't mean they're having thoughts in there

I mean. Not in the way we do, and not with any agency, but I hadn't argued either way on thoughts because I don't know the answer to that.

we know exactly what they're doing inside that vector space -- performing very difficult math that seems totally meaningless to us.

Huh? We know what they are doing but we don't? Yes, we know the math, people wrote it. I coded my first neural network 35 years ago. I understand the math. We don't understand how the math is able to do what LLMs do. If that's what you're saying then we agree on this.

The vectors do not represent concepts. The vectors are math. When the vectors are sent through language decomposition they become words, but they were never concepts at any point.

"The neurons are cells. When neurotransmitters are sent through the synapses, they become words, but they were never concepts at any point."

What do you mean by "they were never concepts"? Concepts of things are abstract. Nothing physical can "be" an abstract concept. If you think about a chair, there isn't suddenly a physical chair in your head. There's some sort of abstract representation. That's what word vectors are. Different from how it works in a human brain, but performing a similar function.

A word vector is an attempt to mathematically represent the meaning of a word.

From this page. Or better still, this article explaining how they are used to represent concepts. Like this is the whole reason vector embeddings were invented.

[–] [email protected] 0 points 1 year ago* (last edited 1 year ago) (16 children)

We do understand how the math results in LLMs. Reread what I said. The neural network vectors and weights are too complicated to follow for an individual, and do not relate on a 1:1 mapping with the words or sentences the LLM was trained on or will output, so individuals cannot deduce the output of an LLM easily by studying its trained state. But we know exactly what they’re doing conceptually, and individually, and in aggregate. Read your own sources from your previous post, that’s what they’re telling you.

Concepts are indeed abstract but LLMs have no concepts in them, simply vectors. The vectors do not represent concepts in anything close to the same way that your thoughts do. They are not 1:1 with objects, they are not a “thought,” and anyway there is nothing to “think” them. They are literally only word weights, transformed to text at the end of the generation process.

Your concept of a chair is an abstract thought representation of a chair. An LLM has vectors that combine or decompose in some way to turn into the word “chair,” but are not a concept of a chair or an abstract representation of a chair. It is simply vectors and weights, unrelated to anything that actually exists.

That is obviously totally different in kind to human thought and abstract concepts. It is just not that, and not even remotely similar.

You say you are familiar with neural networks and AI but these are really basic underpinnings of those concepts that you are misunderstanding. Maybe you need to do more research here before asserting your experience?

Edit: And in relation to your links -- the vectors do not represent single words, but tokens, which indeed might be a whole word, but could just as well be part of a word or an entire phrase. Tokens do not represent the meaning of a word/partial word/phrase, just the statistical use of that word given the data the word was found in. Equating these vectors with human thoughts oversimplifies the complexities inherent in human cognition and misunderstands the limitations of LLMs.

[–] [email protected] 0 points 1 year ago (12 children)

Your concept of a chair is an abstract thought representation of a chair. An LLM has vectors that combine or decompose in some way to turn into the word “chair,” but are not a concept of a chair or an abstract representation of a chair. It is simply vectors and weights, unrelated to anything that actually exists.

Just so incredibly wrong. Fortunately, I'll have save myself time arguing with such a misunderstanding. GPT-4 is here to help:

This reads like a misunderstanding of how LLMs (like GPT) work. Saying an LLM's understanding is "simply vectors and weights" is like saying our brain's understanding is just "neurons and synapses". Both systems are trying to capture patterns in data. The LLM does have a representation of a chair, but it's in its own encoded form, much like our neurons have encoded representations of concepts. Oversimplifying and saying it's unrelated to anything that actually exists misses the point of how pattern recognition and information encoding works in both machines and humans.

[–] [email protected] 0 points 1 year ago (1 children)

Are you kidding me? I sourced GPT4 itself disagreeing with you that it is intelligent and you told me it's lying. And here you are, using it to try to reinforce your point? Are you for real or is this some kind of complicated game?