[danbruc]

I’d argue that all these models are stochastic parrots because they’re not embodied in any way.

I do not think that this would really change much in itself. If you tell the model that crimson is a shade of green, it will learn something wrong whether it has a body or not. What you need is feedback on whether a response is correct or not, factually correct, not grammatically correct. Alternatively you have to teach the model to perform its own fact checking and apply it to its responses.

[AnimalMuppet]

I think that maybe "truly understand" is anchored in the physical world. I don't exhaustively know what, say, grass is, but I know what it looks like, and I know what it feels like to walk on, and I know what it feels like to touch with my hands, and I know what it sounds like when I walk on it, and I know what it smells like when it's cut. And I know that there's a consistent correlation between "stuff that look like that" and "stuff that smells like that when it's cut".

And so if the topic of grass comes up, I have some firsthand knowledge to draw on - less than a botanist, but not nothing. I have some sense impressions that correlate to other sense impressions and to the word "grass". GPT, on the other hand, has some words that correlate to other words, and nothing more.

So it seems fair to say that I understand grass on a level that GPT does not, and cannot. Therefore it seems fair to say that GPT is at least closer to being a stochastic parrot than humans are.

[grumbel]

And yet if you see some AstroTurf you'd still call it grass. In the end there is no "true understanding", there are just predictions we make about the world. Depending on how deeply you look, they are often incorrect, but also generally good enough.

GPT isn't quite at the good-enough point and being limited to only text, makes it impossible to reason about aspects of the world that are difficult to describe in text or simply weren't in the training data.

And more generally speaking, the claim that LLMs don't understand anything really doesn't hold up given how much they are able to hallucinate. If a LLM truly wouldn't understand anything, it wouldn't be able to generate plausible text, it would either generate nonsense or be limited to whatever was in the training data, but that's not the case. The LLMs can predict past their trained knowledge and predict stuff they haven't seen yet. Those predictions will sometimes turn out wrong, but so will the humans prediction that the AstroTurf is grass when taking a closer look.

[danbruc]

Sure, you have experienced grass with more senses than just reading about it, but I do not think that this fundamentally changes anything. If I lied to you your entire life and told you that you are walking on or smelling grass while you were actually walking on moss, you would learn a similar mistake spanning several of your senses.

[AnimalMuppet]

No, I would know moss instead of grass, and I would know it consistently. I would simply have a different label stuck on it than everyone else used.