Yes, but the parent comment used ChatGPT agreeing to their contrarian point as proof of the bot’s capacity for logic, whereas that was likely not what was happening there.
My impression is that ChatGPT has at least as much capacity for logic as most people do in normal life, hence doing better than most humans on the LSATs and other tests that test thinking skills. ChatGPT makes logical errors. So do humans.
Where there is a difference is that there are at least a few humans who can will themselves, with real effort, to proceed in a very, very rigorously logical way, which ChatGPT does not do. However, the abilities of AIs in that regard should not be judged by the very first iteration of AIs that can do well on the LSATs. It should be judged by what's coming. And you can bet that what's coming includes AIs that can consistently think in a way that is far faster and far more rigorously logical than the best humans, and which can apply that speed and rigor to any subject area. Those will probably not be pure LLMs, although my guess is that the earliest ones will be variants of existing LLMs with the appropriate capabilities added on. Like a human using a calculator, an LLM could call a logic module.
Or perhaps, if the goal is only to be almost always better at logical tasks than even the most capable humans, all that is needed is to have some fine-tuning so that, in certain circumstances, they do something akin to what humans do when they will themselves to be rigorously logical for a particular task.
The controversy over chatgpt standardized test performance is this:
To some extent, logic can cover lack of knowledge, and vice versa. Pattern matching mixes in too.
ChatGPT has incredible knowledge abd also pattern matching, and terible logic. (But a pretty good pseudo logic based on human language patterns, including human reasoning in written form.)
Chat got does well on tests using its incredible knowledge to cover it's lack of basic logical ability.
I think you make a good point, except that I strongly suspect that when humans write software, etc. etc., they, too, are relying on patterns stored in their memories more than they are performing "fresh logic".
This is my impression, as someone who writes software professionally (staring in the 80's) and is now using ChatGPT as an assistant. I count myself in the group of people that don't use fresh logic all that often in coding. It's pretty rare that ChatGPT couldn't do the same things I do, and I see no reason to think I'm doing them in a more purely-logical way. At least not the vast majority of the time.
But I think you're making the point that humans at least have the ability to perform fresh logic, whereas ChatGPT may not. Maybe we differ in where the cutoff is that humans actually use that ability. I think it's pretty rare. I submit that it resides in times when people make the conscious decision to very consciously follow a series of very simple logical steps. That takes effort. It's not natural to us, although it may be more natural to some people than others. And I think that most people, most of the time, rely on pattern-based pseudo-logic instead of doing that.
Isn't the paper discussing precisely the opposite? That chatgpt predicts text in a way that, with each version, resembles more and more human logic, both with its succeseses and errors?
Where there is a difference is that there are at least a few humans who can will themselves, with real effort, to proceed in a very, very rigorously logical way, which ChatGPT does not do. However, the abilities of AIs in that regard should not be judged by the very first iteration of AIs that can do well on the LSATs. It should be judged by what's coming. And you can bet that what's coming includes AIs that can consistently think in a way that is far faster and far more rigorously logical than the best humans, and which can apply that speed and rigor to any subject area. Those will probably not be pure LLMs, although my guess is that the earliest ones will be variants of existing LLMs with the appropriate capabilities added on. Like a human using a calculator, an LLM could call a logic module.
Or perhaps, if the goal is only to be almost always better at logical tasks than even the most capable humans, all that is needed is to have some fine-tuning so that, in certain circumstances, they do something akin to what humans do when they will themselves to be rigorously logical for a particular task.