this post was submitted on 21 Sep 2023
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submitted 1 year ago* (last edited 8 months ago) by CoderSupreme@programming.dev to c/asklemmy@lemmy.ml
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[–] LeastIHaveChicken@lemmy.world 11 points 1 year ago* (last edited 1 year ago) (1 children)
[–] lvxferre@lemmy.ml 14 points 1 year ago* (last edited 1 year ago) (2 children)

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).

[–] bionicjoey@lemmy.ca 4 points 1 year ago

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.

[–] Even_Adder@lemmy.dbzer0.com 4 points 1 year ago (1 children)

Have you seen this paper:

Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this question by applying a variant of the GPT model to the task of predicting legal moves in a simple board game, Othello. Although the network has no a priori knowledge of the game or its rules, we uncover evidence of an emergent nonlinear internal representation of the board state. Interventional experiments indicate this representation can be used to control the output of the network and create "latent saliency maps" that can help explain predictions in human terms.

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.

[–] lvxferre@lemmy.ml 5 points 1 year ago* (last edited 1 year ago) (1 children)

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:

  1. It boils down to appeal to ignorance - "we don't know, it's a blackbox system, so let's assume that some property (conceptualisation) is there, even if there are other ways to explain the phenomenon (output)".
  2. Hallucinations themselves provide evidence against any sort of conceptualisation. Specially when the bot contradicts itself.

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:

If we think of a board as the β€œworld,” then games provide us with an appealing experimental testbed to explore world representations of moderate complexity

This setting allows us to investigate world representations in a highly controlled context

Our next step is to look for world representations that might be used by the network

Othello makes a natural testbed for studying emergent world representations

To systematically determine if this world representation

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.

[–] Even_Adder@lemmy.dbzer0.com 2 points 1 year ago* (last edited 1 year ago) (1 children)

Interesting take, what do you make of this one?

[–] lvxferre@lemmy.ml 4 points 1 year ago (1 children)

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.

[–] Even_Adder@lemmy.dbzer0.com 3 points 1 year ago (1 children)

This video gives a decent explanation of what might be going on with the hands if you're interested.

[–] lvxferre@lemmy.ml 2 points 1 year ago

Thanks - I'll check it out.