Yes people are claiming different things yet no definitive proof has been offered given the varying findings. I can cite another 3 papers which agree with my point and you can probably cite just as many if not more supporting yours. I’m arguing against people depicting what is not a forgone conclusion as such. It seems like in people’s rush to confirm their own preconceived notions people forget that, although a theory may be convincing, it may not be true. Evidence in this very thread of a well-known SOTA LLM not being able to tell which is greater between two numbers indicates to me that what is being called “reasoning” is not what humans do. We can make as many excuses we want per the tokenizer or whatever but then forgive me for not buying the super or even general “intelligence” of this software. I still like these tools though, even if I have to constantly vet everything they say as they often tend to just outright lie, or perhaps more accurately: repeat lies in their training data even if you can elicit a factual response on the same topic.
I can’t and that’s pretty cool to think about! Of course if we’re going that far down the chain of assumption we’re not quite ready to talk about LLMs imo (then again maybe it would be the perfect place to talk about them as contrast/comparison; certainly exciting ideas in that light).
From my own perspective: if we’re gonna say these things reason and we’re using the definition of reasoning we apply to humans, then being able to reason through the trivial cases they fail to today would be a start. To the proponents of “they reason sometimes but not others” my question is why? What reason does it have to not reason and why if it is reasoning it still fails on trivial things that are variations of its own training data? I would also expect that these models would use reasoning to find new things like humans do but without humans essentially guiding the model to the correct awnser or the model just brute-forcing a problem-space with a set of rules/heuristics. Not exhaustive but a good start I think. These models have trouble currently even doing the advertised things like “book a trip for me” once a UI update happens so I think it’s a great indication we don’t quite have the intelligence/reasoning aspect worked out.
Another question I have: would a form of authentic reasoning in a model give rise to a model having an aesthetic? Could this be some sort of indicator of having created a “model of the world”? Does the model of the world perhaps imply a value judgement about it given that if one was super intelligent wouldn’t one of the first things realized be the limitations of its own understanding even given the restrictions of time and space and not ever potentially being able to observe the universe in its entirety? Perhaps a perfect super intelligence would just evaporate/transcend like in the Culture series. What a time to be alive!
IMHO, any argument against LLM intelligence should be validated by first applying them to humans.
And then you'd realize that a lot of naïve arguments against LLMs would imply that a significant portion of homo-sapiens can't reason, are unable to really think, and are no more than stochastic parrots.
It's actually a rather dangerous line of reasoning.
I’m curious what’s dangerous about it? How do your square the inability to play tic-tac toe or do value comparisons correctly with “we should compare this to a humans reasoning”
If it can’t do things like basic value comparison correctly what business do we have saying it “reasons like a human?”
The danger is when LLMs start to outperform humans on many tasks (which they already have), claiming that LLMs are stochastic parrots could be seen to imply that less intelligent people are also no better than stochastic parrots.
