[qarl]

> The paper doesnt mention the models coming up with the algorithm at all AFAIK.

And that's because they specifically hamstrung their tests so that the LLMs were not "allowed" to generate algorithms.

If you simply type "Give me the solution for Towers of Hanoi for 12 disks" into chatGPT it will happily give you the answer. It will write program to solve it, and then run that program to produce the answer.

But according to the skeptical community - that is "cheating" because it's using tools. Nevermind that it is the most effective way to solve the problem.

https://chatgpt.com/share/6845f0f2-ea14-800d-9f30-115a3b644e...

[zoul]

This is not about finding the most effective solution, it’s about showing that they “understand” the problem. Could they write the algorithm if it were not in their training set?

[qarl]

That's an interesting question. It's not the one they are trying to answer, however.

From my personal experience: yes, if you describe a problem without mentioning the name of the algorithm, an LLM will detect and apply the algorithm appropriately.

They behave exactly how a smart human would behave. In all cases.

[boredhedgehog]

If that's the point, shouldn't they ask the model to explain the principle for any number of discs? What's the benefit of a concrete application?

[johnecheck]

Because that would prove absolutely nothing. There are numerous examples of tower of Hanoi explanations in the training set.

[elbear]

How do you check that a human understood it and not simply memorised different approaches?

[YeGoblynQueenne]

You ask them to solve several instances of the problem?

[godelski]

It's hard. But usually we ask several variations and make them show their work.

But a human also isn't an LLM. It is much harder for them to just memorize a bunch of things, which makes evaluation easier. But they also get tired and hungry, which makes evaluation harder ¯\_(ツ)_/¯

[elbear]

If we're talking about solving an equation, for example, it's not hard to memorize. Actually, that's how most students do it, they memorize the steps and what goes where[1].

But they don't really know why the algorithm works the way it does. That's what I meant by understanding.

[1] In learning psychology there is something called the interleaving effect. What it says is that you solve several problems of the same kind, you start to do it automatically after the 2nd or the 3rd problem, so you stop really learning. That's why you should interleave problems that are solved with different approaches/algorithms, so you don't do things on autopilot.

[Too]

How can one know that's not coming from the pre-trained data. The paper is trying to evaluate whether the LLM has general problem solving ability.