Veraticus

Oh, you again -- it's incredibly ironic you're talking about wrong statements when you are basically the poster child for them. Nothing you've said has any grounding in reality, and is just a series of bald assertions that are as ignorant as they are incorrect. I thought you would've picked up on it when I started ignoring you, but: you know nothing about this and need to do a ton more research to participate in these conversations. Please do that instead of continuing to reply to people who actually know what they're talking about.

So, no point? Cool.

Did you have a point or are you only trying to argue semantics?

This is a great article, thanks for linking it!

Yeah, that would be a good usage of an LLM!

You used the term and I was using it with the same usage you were. Why are you quibbling semantics here? It doesn’t change the point.

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.

No, they learn English (or any other language) from humans. Translation requires a Rosetta Stone and LLMs are still much worse at such tasks than dedicated translation programs.

Edit: I guess if you are suggesting that the LLM could become an LLM of the dead language and communicate only in said dead language, that is indeed possible. Since users would need to speak that dead language to communicate with it though I don’t understand the utility of such a thing (and is certainly not what the author meant anyway).

LLMs do not grow up. Without training they don’t function properly. I guess in this aspect they are similar to humans (or dogs or anything else that benefits from training), but that still does not make them intelligent.

LLMs can't do any of those things though...

If no one teaches them how to speak a dead language, they won't be able to translate it. LLMs require a vast corpus of language data to train on and, for bilingual translations, an actual Rosetta stone (usually the same work appearing in multiple languages).

This problem is obviously exacerbated quite a bit with animals, who, definitionally, speak no human language and have very different cognitive structures to humans. It is entirely unclear if their communications can even be called language at all. LLMs are not magic and cannot render into human speech something that was never speech to begin with.

The whole article is just sensationalism that doesn't begin to understand what LLMs are or what they're capable of.

Large language models by themselves are “black boxes”, and it is not clear how they can perform linguistic tasks. There are several methods for understanding how LLM work.

You are misunderstanding both this and the quote from Anthropic. They are saying the internal vector space that LLMs use is too complicated and too unrelated to the output to be understandable to humans. That doesn't mean they're having thoughts in there: we know exactly what they're doing inside that vector space -- performing very difficult math that seems totally meaningless to us.

Is this not what word/sentence vectors are? Mathematical vectors that represent concepts that can then be linked to words/sentences?

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.

What a silly assertion. Eliza was simulating conversations in the 80s; it was no more intelligent than the current crop of chatbots.