[willbudd]

Au contraire. Learning an abstract logical relationship such as line of succession during training, and then applying substitution/reification during inference to deduce the new factual clause that Charles is king of the UK is exactly what it means to learn something new. It's just a pity it can't memorize this fact at inference time, and that won't be able to reproduce it as soon as the information about the queen's death slides outside of the context window.

[geraneum]

That’s actually correct but an overfitted definition for learning. It holds certain hidden assumptions (i.e physical grounding) of the learner being human which makes it inapplicable to an LLM. As in a self driving car which passes a driving exam but fails to drive effectively freely in the city (it’s not an LLM but relevant in this context). You have to admit when you work with this tech that something fundamental is missing in how they perform.

[willbudd]

> That’s actually correct but an overfitted definition for learning. It holds certain hidden assumptions (i.e physical grounding) of the learner being human which makes it inapplicable to an LLM.

Inapplicable why exactly? Because you say so? Logic isn't magic. Nor is learning. No (external) grounding is required either: iteratively eliminating inconsistent world models is all you need to converge toward a model of the real world. Nothing especially human or inhuman about it. LLM architecture may not be able to represent a fully recursive backtracking truth maintenance system, but it evidently managed to learn a pretty decent approximation anyway.

[geraneum]

> Because you say so?

Chill my friend, no need to get personal. We are talking about ideas. It’s OK to disagree. I am simply dismissing your initial claim. This usually happens when you present a scientific argument based on personal beliefs. If it’s not magic, then we should be able to doubt and examine it and it should eventually pass scientific muster.

> No grounding is required… It evidently managed to learn a pretty decent approximation.

Well, last time I used an LLM it suggested that I should lift the chair I am sitting in. I guess OpenAI has a lot of work to do. They have to eliminate this inconsistent world model for chairs, tables, floor, My dog, my cat and all the cats living on Mars…

edit: added a missing word.

[willbudd]

Wasn't intended to be personal. Just a mediocre way of expressing that your assertion there is missing any form of argumentation, and therefore as baseless as it is unconvincing.

I'm seeing an emergent capability of encoding higher order logic, and the whole point of such abstractions is to not need to hardcode your weights with the minutiae of cats on Mars. LLMs today are only trained to predict text, so it's hardly surprising that they have some gaps in their understanding of Newtonian physics. But that doesn't mean the innate capability of grasping such logic isn't there, waiting for the right training regime to expose it to its own falling apples, so to speak.

[crakenzak]

I'm curious if future developments in LLMs will enable them to extract significant/noteworthy info from their context window and incorporate it into their underlying understanding by adjusting their weights accordingly. This could be an important step towards achieving AGI, since it closely mirrors how humans learn imo.

Humans continually update their foundational understanding by assimilating vital information from their "context window" and dumping irrelevant noise. If LLMs could emulate this, it would be a huge win.

Overall, very exciting area of research!