by aspenmartin 4 hours ago
> Reasoning includes things like proper use of logic. LLMs have been repeatedly shown to fail horribly at this.
That models cannot do ALL logic problems does not mean that they cannot properly use logic...they can write Lean-verified theorems. How is that not logic?
> They consistently fail at drawing basic logical conclusions because they cannot build a sufficiently abstract model of certain problems that allows them to grasp their true nature.
What does their "grasp[ing] their true nature" have anything to do with what they can do?
> In other words, the whole class of questions of the kind of "how many r's in strawberry" or "do I take the car to the car wash?" would be answered correctly and reliably.
Again, just because you have interesting failure modes or brittleness does not mean they do not reason.
This is exactly backwards. The brittleness is because they emulate reasoning without actually algorithmically performing it.
Add.: I pointed to this class of problems specifically because they require the ability to abstract in a way that the question itself does not immediately suggest. Math problems are different in that they are described in terms of art that are closely related to certain patterns of manipulation (that is, the paper texts tend to contain both in close proximity to one another).
For you, a system needs to reason perfectly and flawlessly, all the time? So humans do not reason? Humans don't have brittle failure modes?
> they require the ability to abstract in a way that the question itself does not immediately suggest
yes, yet there are multitudes of other measurements of the same kind where LLMs reason perfectly well and better in many cases than a human could.
> Math problems are different in that they are described in terms of art that are closely related to certain patterns of manipulation (that is, the paper texts tend to contain both in close proximity to one another).
Is your logic really that math problems are actually easier to answer without reasoning and just by blending together closely related papers? I would definitely suggest reading the literature a bit more on this topic.