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> 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 dimensi

> 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 when presented with the most outlandish cases.

RandomLensman 3y ago
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.
- moffkalast 3y ago
  Well for me personally, the proof is in giving it a few sentences on how it should write fairly complicated pieces of unique code I need on a daily basis and seeing it correctly infer things I forgot to specify in ways that are typically borderline impossible for anything but another human. If that's not reasoning I don't know what is. The other one that convinced me was this list: https://i.imgur.com/CQlbaDN.png 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 https://www.youtube.com/watch?v=j6O_uePUKKI
  - RandomLensman 3y ago
    Coding isn't a use case of mine. For example, for things like financial derivatives replication it can tell you the abstract concept but it cannot apply it in a meaningful way.

RandomLensman 3y ago

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.

moffkalast 3y ago
Well for me personally, the proof is in giving it a few sentences on how it should write fairly complicated pieces of unique code I need on a daily basis and seeing it correctly infer things I forgot to specify in ways that are typically borderline impossible for anything but another human. If that's not reasoning I don't know what is. The other one that convinced me was this list: https://i.imgur.com/CQlbaDN.png 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 https://www.youtube.com/watch?v=j6O_uePUKKI
- RandomLensman 3y ago
  Coding isn't a use case of mine. For example, for things like financial derivatives replication it can tell you the abstract concept but it cannot apply it in a meaningful way.