this post was submitted on 21 Sep 2023
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Is that not what we do?
No. For two reasons:
And some might say "hey, look at this output, it resembles human output!", but the difference gets specially obvious when either side gets things wrong (human brainfarts still show some sort of purpose and conceptualisation; LLM hallucinations are often plain gibberish).
For anyone doubting this, I encourage you to have GPT generate some riddles for you. It is remarkable how quickly the illusion is broken. Because it doesn't understand enough about the concepts underpinning a word to create a good riddle.
Have you seen this paper:
https://arxiv.org/abs/2210.13382
It has an internal representation of the board state, despite training on just text. Leading to gpt-3.5-turbo-instruct beating chess engine Fairy-Stockfish 14 at level 5, putting it at around 1800 ELO.
I didn't read this paper (I'll update this comment once I read it), but this sort of argument relying on emergent properties pops up from time to time, to claim that the bots are handling something further than tokens. It's weak for two reasons:
And note that the argument does not handle the lack of pragmatic purpose of the bot utterances. At all.
Specifically regarding games, what I think that is happening is that the bot is handling some logic based on the tokens themselves, in order to maximise the probability of a certain output (e.g. "you won"). That isn't even remotely surprising, and it doesn't require any sort of further abstraction to explain.
EDIT, from the paper:
Are you noticing the pattern? The researchers are taking for granted that the model will have some sort of "world representation", as an unspoken premise. With no hβ like "no such thing".
And at the end of the day they proved that a chatbot can perform the same sort of logical operations that a "proper" game engine would.
Interesting take, what do you make of this one?
I think that image models are a completely different beast from language models, and I'm simply not informed enough about image models. So take what I'm going to say with a grain of salt.
I think that it's possible that image models do some sort of abstraction that resembles how humans handle images. Including modelling a third dimension not present in a 2D picture, or abstractions like foreground vs. background. If it does it or not, I don't know.
And unlike for language models, the image model hallucinations (e.g. people with six fingers) don't seem to contradict the idea that the model still recognises individual objects.
This video gives a decent explanation of what might be going on with the hands if you're interested.
Thanks - I'll check it out.