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[0] https://news.ycombinator.com/item?id=35442147

Here was my test at ChatGPT 3.5.[0] I made up a novel game, and it figured it out. The test is simple, but it made me doubt absolute arguments that LLMs are not able to reason, in some way.

There is a question at the end of that comment, would love to hear other options.

How does this prove reasoning? The thread you point to has several question in it that remain unanswered that ask the same question? How is this not entirely derivative too — there’s a huge number of these kind of 3-box “games” (although I don’t see this as a game really) so something very similar to this is probably in the training data a lot. Writing code to factor a number is definitely very common. Variation of this are also very common interview questions for interns (at least when I was interviewing)

Here is GPT 4o in "reasoning" mode answering the question "Are LLMs capable of reasoning?"

> It depends on how you define "reasoning."

> LLMs like me can perform certain types of reasoning, such as:

> Pattern recognition & analogy – Recognizing relationships between concepts and applying similar patterns in new contexts.

> Deductive reasoning – Applying rules to specific cases (e.g., "If all humans are mortal and Socrates is a human, then Socrates is mortal").

> Inductive reasoning – Generalizing from specific examples (though often with statistical biases).

> Common-sense reasoning (to an extent) – Inferring likely outcomes based on broad training data.

> However, there are limitations:

> No true understanding – LLMs don’t "know" things like humans do; they predict based on probabilities . > Struggles with multi-step logic – Complex reasoning that requires deep memory or long-term planning is difficult.

> Lack of real-world interaction – Without direct experience, reasoning is limited to text-based patterns.

> Confidence: 90%

> (Strong evidence suggests LLMs can perform certain types of reasoning, but they are not yet comparable to human-level reasoning.)

Would you agree with that analysis? If so, then LLMs are indeed capable of reasoning, in some ways.

tripzilch 495 days ago

It fails at deductive reasoning though. Pick a celebrity with non-famous children that don't obviously share their last name or something. If you ask it "who is the child of <celebrity>", it will get it right, because this is in its training data, probably Wikipedia.

If you ask "who is the parent of <celebrity-child-name>", it will often claim to have no knowledge about this person.

Yes sometimes it gets it right, but sometimes also not. Try a few celebrities.

Maybe the disagreement is about this?

Like if it gets it right a good amount of the time, you would say that means it's (in principle) capable of reasoning.

But I say, that if it gets it wrong a lot of the time, that means 1) it's not reasoning in situations when it gets it wrong, but also 2) it's most likely also not reasoning in situations when it gets it right.

And maybe you disagree with that, but then we don't agree on what "reasoning" means. Because I think that consistency is an important property of reasoning.

I think that if it gets "A is parent of B, implies B is child of A" wrong for some celebrity parents, but not for others, then it's not reasoning. Because reasoning would mean applying this logical construct as a rule, and if it's not consistent at that, it makes it hard to argue that it is in fact applying this logical rule instead of doing who-knows-what that happens to give the right answer, some of the time.

https://en.wikipedia.org/wiki/Logical_reasoning#Analogical

I was unable to find my exact "game" in google's index.

Therefore, how does my example not qualify as this, at least:

> Analogical reasoning involves the comparison of two systems in relation to their similarity. It starts from information about one system and infers information about another system based on the resemblance between the two systems.

Is it actually reasoning though or just pattern matching? Seems like to compare one should also “know” which your above response indicates they do not.

I guess the real question is “does moving down a stochastic gradient of probabilities suffice as reasoning to you” and my awnser is no because you don’t need reason to find the nearest neighbor in this architecture. In this case the model is not actively comparing and inferring its simply associating without “knowing”

There are many types of reasoning, and LLMs appear to do some of them.

Repeating a point without proffering evidence only makes it seems as if you don’t have anything to argue of substance.

ninetyninenine 497 days ago

My thread has been voted down and it’s getting stale. The few remaining people are biased towards there point of view and are unlikely to entertain anything that will trigger a change in their established world view.

Most people will use this excuse to avoid responding to or even looking at your link here. It is compelling evidence.

I’d settle for these things being able to do value comparison consistently well, play a game of tic tac toe more than once correctly or use a UI after an update and not fail horrendously to move the needle a little bit for me. People claiming these things selectively reason while also not being able to explain why seems a lot like magical thinking to me rather than entertaining the possibility you might be projecting onto something that is really damn-well engineered to make your anthropomorphize it.

ninetyninenine 498 days ago

I can prove LLMs can reason. You cannot prove LLMs can't reason. This is easily demonstrable. LLMs failing to reason is not proof LLMs can't reason, it's just proof that an LLM didn't reason for that prompt.

All I have to do is show you one prompt with a correct answer that cannot be arrived at with pattern matching and the prompt can only be arrived at through reasoning. One. You have to demonstrate this for EVERY prompt if you want to prove LLMs can't reason.

cristiancavalli 498 days ago

No I can “prove” it — look at any number of cases where LLMs can’t even do basic value comparisons despite being claimed as super intelligent. You can try and say well that’s a limitation of the technology and then I would reply — yes and that’s why I would say it’s not reasoning according the original human definition. Also you have yet to produce any evidence of reasoning and claiming you can over and over again doesn’t add to your arguments substance. I would be interested in your proof that some answer can’t be pattern matched too — at this point I wonder if we could create an non conscious “intelligence” that if large enough would be mostly able to describe anything known to us along some line of probability we couldn’t compute with our brain architecture and it could be close to 99.99999% right. Even if we had this theoretical probability-based super intelligence it still wouldn’t be “reasoning” but could be more “intelligent” than us.

I’m also not entirely convinced we can’t arrive at a reasoning system via probability only (a really cool thought experiment) but these systems do not meet the consistency/intelligence bar for me to believe this currently.

ninetyninenine 498 days ago

LLMs can reason they just don’t always reason.

That’s the claim everyone makes. That is a human definition if it reasoned one time correctly. That is the colloquial definition.

Someone who has brain damage can reason correctly on certain subjects and incorrectly on other subjects. This is an immensely reasonable definition. I’m not being pedantic or out of line here when I say LLMs can reason while using this definition.

Nobody is making the claim that LLMs reason like humans or are human or reason perfectly every time. Again the claim is: LLMs are capable of reasoning.

tripzilch 495 days ago

No reasoning is about applying rules of logic consistently, so if you only do it some of the time, that's not reasoning.

If I roll a die and only _sometimes_ it returns the correct answer to a basic arithmetic question, this is the exact reason why we don't say a die is doing arithmetic.

Even worse in the case of LLMs, where it's not caused by pure chance, but also training bias and hallucinations.

You can claim nobody knows the exact definition of reasoning, maybe there are some edges which aren't clearly defined because they're part of Philosophy, but applying rules of logic consistently is not something you just don't always do and still call it reasoning.

Also, LLMs are generally incapable of saying they don't know something, cannot know something, can't do something, etc. They would rather try and hallucinate. When it does that, it's not reasoning. And you also can't explain to an LLM how to figure out it doesn't know something, and then actually say it doesn't know and not make stuff up. If it was capable of reasoning you should be able to convince it using _reason_, to do exactly that.

However, you

cristiancavalli 498 days ago

I still think the jury is out on this given that they seem to fail on obvious things which are trivially reasoned about by humans. Perhaps they reason differently at which point I would need to understand how this reasoning is different from a humans reasoning (perhaps biological reasoning more generally?) and then I would want to consider whether one ought to call it reasoning given its differences (if there are any at the time of sampling). I understand your claim I’m just not buying it based on the current evidence and my interacting with these supposed “super intelligences” every day. I still find these tools valuable, just unable to “reason” about a concept which makes me think, as powerful and meaning filled as language is, our assumption of reasoning might just be a trick of our brain reasoning through a more tightly controlled stochastic space and us projecting the concept of reasoning onto a system. I see the COT models contort and twist language in a simulacrum of “reasoning” but any high school English teacher can tell you there is a lot of text written that appears to logically reason but doesn’t actually do anything of the sort once read with the requisite knowledge in the subject matter.

ninetyninenine 498 days ago

They can fail at reasoning. But they can demonstrably succeed to.

So the the statement that they CAN reason is demonstrably true.

Ok if given a prompt where the solution can only be arrived at by reasoning and the LLM gets to the solution for that single prompt, then how can you say it can't reason?

cristiancavalli 498 days ago

Given your set of theoreticals then I would concede, yes the model is reasoning. At that point, though, the world would probably be far more concerned with your finding of a question that can only be met via reasoning and would be uninfluenced or paralleled by any empirical phenomenon including written knowledge as a medium of transference. The core issue I see here is you being able to prove that the model is actually reasoning in a concrete way that isn’t just a simulacrum like the Apple researchers et al. theorize it to be.

If you do find this question answer pair then it would be a massive breakthrough for science and philosophy more generally.

You say “demonstrably” but I still do not see a demonstration of these reasoning abilities that is not subject to the aforementioned criticisms.

JackSlateur 498 days ago

Just say it : llm are random machine. Even a broken clock is right twice a day.