| If your "proof" can't port to Humans then it's not proof Learn to take a hint. I'm not going to argue this on human terms because you're playing a dumb um-akshually game. Computer reasoning systems can solve vastly more complex problems perfectly. Expert mathematicians can solve vastly more complex problems with only minimally increased errors. The ability of LLMs to solve reasoning problems completely disintegrates when the problems get more complex. Trying to argue that LLMs are alike humans because of you can put these three into the buckets of "No mistakes" and "Some mistakes" is ridiculous. Nobody is calling LLMs perfect reasoning machines. Yes. You said humans make mistakes, my point here is, humans make mistakes precisely because they stop doing reasoning and start doing blind pattern matching estimation of the answer. The idea that you must make no mistake reasoning before you can be considered to be reasoning has no ground. Reading comprehension. I did not say no mistakes. I said that the failure pattern follows that of estimated guesses; Rapidly increasing errors as the size of the problem increases. Whereas with computer reasoning, the rate of errors does not increase at all. And with (expert) humans the rate only goes up a little. Did you even bother looking at the link? You are missing the point. I am not referring to literally English or any other language. I'm referring to the structure of language problems, which is vastly simpler than any moderately complex math or programming problem. To more explicitly spell out the reason for my unimpressed-ness: They trained a pattern-repeating-machine and found that it will repeat some of their patterns, some of which were patterns trained on. This does not demonstrate the ability to reason abstractly about new models, so I do not care. |