Earlier quoted context omitted.
> If you want to test world modeling, give it objects it will have never encountered, describe them and then ask to stack etc. For example, a bunch of 7 dimensional objects that can only be stacked a certain way. And when it does that perfectly, I assume you'll say that was also in the training data? All examples I've seen or tried point to LLMs being able to do some kind of reasoning that is completely dynamic, even…
All examples I tried myself show it failing miserable at reasoning. It certainly needs better evidence than being able to come up with one of many possibilities of stacking things - aided by human interpretation on top of the text output. Happy to look at other suggestions for test problems.
The other one that convinced me was this list: https://i.imgur.com/CQlbaDN.png I think the leetcode tests are quite indicative, going as far as saying that GPT-4 scores 77% on basic reasoning, 26% on complex reasoning and 6% on extremely complex reasoning.
Maybe the reasoning is all "baked in" as it were, like in a hypothetical machine doing string matching of questions and answers with a database containing an answer to every possible question. But in the end, correctly using those baked in thought processes may be good enough for it to be completely indistinguishable from the real thing, if the real thing even exists and we aren't stochastic resamplers ourselves.
> aided by human interpretation on top of the text output
That's an interesting point actually, I've been trying to do something in that regard recently, by having it use an API to do actual things (in a simulated environment) and it seems very promising despite the model not being tuned for it, but given that AutoGPT and plugin usage are a thing, that should be all the evidence you need on that front.
Google also did this with their old Palm model which is vastly inferior to even GPT 3.5: https://www.youtube.com/watch?v=j6O_uePUKKI