Current LLMs have gotten better at this with each iteration without the extensive rote repetition of applying the algorithms following by additional training on corrections that we subject people to.

Yet, still, I think you overestimate the average humans ability to reliably execute even algorithms that simple.

I've had LLMs do that correctly up to far larger numbers than I've multiplied by hand. I could do it, but I'm not confident I wouldn't get sloppy and make mistakes without going back and reviewing my work.

I've also had LLMs execute algorithms most people wouldn't even understand. E.g. converting between NFAs and DFAs, or taking a grammar in an unspecified BNF variant abd generate grammatically valid sentences in it, or conversely validating a sentence against the grammar of a fictional language.

I think a whole lot of the arguments here assume a level of human reasoning well above average.

That's not to say current LLMs do not also have glaring blind spots, but so it seems to me does people dismissing their apparent reasoning out of hand because of assumptions about human abilities we mostly haven't tested.

> I've had LLMs do that correctly up to far larger numbers than I've multiplied by hand.

but given enough time you can multiply any numbers, while LLM has fundamental inability to learn algorithm.

> I've also had LLMs execute algorithms most people wouldn't even understand. E.g. converting between NFAs and DFAs

this is a not good quality discussion, such claims should be expressed in the form of some research results: describing what you do exactly, how data has been obtained, verified for correctness, what is complexity of your NFAs etc, otherwise I personally have hard time to judge how indicative your claim.

ChatGPT knows how to write Python code, run it, and use the results.

It can also use WolframAlpha. It’s not a pure LLM solution but it’s good enough to multiply numbers.

Have you tried it?

ChatGPT knows how to multiply step by step without either of those, but like a human, who'll be annoyed and usually sloppy if you ask them to do things step by step instead of taking shortcuts, you increasingly need to push it to get it to do so with care.

The irony of these objections is that people speak out of a "but we learned to do that at school" way of thinking, often on the basis of above average skills, rather than considering that while most people maybe could (I'm not convinced), most people won't do it step by step unless you really goad them into it and pay attention that they don't skip or gloss over steps.

The question is inability of ChatGPT to do logical reasoning. Multiplying numbers is a simple test to prove this.

The same with coding: it can produce code for tasks where something similar was in training dataset, but if you ask to do completely unknown problem - it will fail.

> but given enough time you can multiply any numbers, while LLM has fundamental inability to learn algorithm.

GPT4 at least knows the algorithms - many alternative version - and will follow them. What it won't do, just like most humans, is take the time to go over it and review each step for possible errors without further promoting and inducement.

Yes, humans could. Most humans don't, without outside pressure. With pressure applied and LLM will too.

Again, this is an example of how people overestimate how humans do in the same condition.

> this is a not good quality discussion, such claims should be expressed in the form of some research results: describing what you do exactly, how data has been obtained, verified for correctness, what is complexity of your NFAs etc, otherwise I personally have hard time to judge how indicative your claim

I'll happily do that if you do the same for your unsubstantiated claims first.

Otherwise you can simply actually try to ask ChatGPT about these things yourself, and then try to ask a random person about them.

> With pressure applied and LLM will too.

unless it is fundamentally incapable.