You can check my post history to see how unpopular this point of view is, but the big "reveal" that will come up is as follows:
The way that LLMs and humans "think" is inherently different. Giving an LLM a test designed for humans is akin to giving a camera a 'drawing test.'
A camera can make a better narrow final output than a human, but it cannot do the subordinate tasks that a human illustrator could, like changing shadings, line width, etc.
An LLM can answer really well on tests, but it often fails at subordinate tasks like 'applying symbolic reasoning to unfamiliar situations.'
Eventually the thinking styles may converge in a way that makes the LLMs practically more capable than humans on those subordinate tasks, but we are not there yet.
The way that LLMs and humans "think" is inherently different. Giving an LLM a test designed for humans is akin to giving a camera a 'drawing test.'
A camera can make a better narrow final output than a human, but it cannot do the subordinate tasks that a human illustrator could, like changing shadings, line width, etc.
An LLM can answer really well on tests, but it often fails at subordinate tasks like 'applying symbolic reasoning to unfamiliar situations.'
Eventually the thinking styles may converge in a way that makes the LLMs practically more capable than humans on those subordinate tasks, but we are not there yet.