Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
We have already thrown just about all the Internet and then some at them. It shows that LLMs can not think or reason. Which isn’t surprising, they weren’t meant to.
Or at least they can’t reason the way we do about our physical world.
No, they cannot reason, by any definition of the word. LLMs are statistics-based autocomplete tools. They don’t understand what they generate, they’re just really good at guessing how words should be strung together based on complicated statistics.
You seem pretty sure of that. Is your position firm or are you willing to consider contrary evidence?
Definition: https://www.wordnik.com/words/reasoning
Evidence or arguments used in thinking or argumentation.
The deduction of inferences or interpretations from premises; abstract thought; ratiocination.
Evidence: https://lemmy.world/post/43503268/22326378
I believe this clearly shows the LLM can perform something functionally equivalent to deductive reasoning when given clear premises.
“Auto-complete” is lazy framing. A calculator is “just” voltage differentials on silicon. That description is true and also tells you nothing useful about whether it’s doing arithmetic.
The question of whether something is or isn’t reasoning isn’t answered by describing what it runs on; it’s answered by looking at whether it exhibits the structural properties of reasoning: consistency across novel inputs, correct application of inference rules, sensitivity to logical relationships between premises. I think the above example shows something in that direction. YMMV.
I can be convinced by contrary evidence if provided. There is no evidence of reasoning in the example you linked. All that proved was that if you prime an LLM with sufficient context, it’s better at generating output, which is honestly just more support for calling them statistical auto-complete tools. Try asking it those same questions without feeding it your rules first, and I bet it doesn’t generate the right answers. Try asking it those questions 100 times after feeding it the rules, I bet it’ll generate the wrong answers a few times.
If LLMs are truly capable of reasoning, it shouldn’t need your 16 very specific rules on “arithmetic with extra steps” to get your very carefully worded questions correct. Your questions shouldn’t need to be carefully worded. They shouldn’t get tripped up by trivial “trick questions” like the original one in the post, or any of the dozens of other questions like it that LLMs have proven incapable of answering on their own. The fact that all of those things do happen supports my claim that they do not reason, or think, or understand - they simply generate output based on their input and internal statistical calculations.
LLMs are like the Wizard of Oz. From afar, they look like these powerful, all-knowing things. The speak confidently and convincingly, and are sometimes even correct! But once you get up close and peek behind the curtain, you realize that it’s just some complicated math, clever programming, and a bunch of pirated books back there.
You’re failing into the same trap. When the letters on the screen tell you something, it’s not necessarily the truth. When there is “I’m reasoning” written in a chatbot window, it doesn’t mean that there is a something that’s reasoning.