3 ms·

> It’s patently obvious that LLMs can reason and solve novel problems not in their training data. Would you care to tell us more ? « It’s patently obvious »

> It’s patently obvious that LLMs can reason and solve novel problems not in their training data. Would you care to tell us more ? « It’s patently obvious » is not really an argument, I could say just as well that everyone know LLM can’t resonate or think (in the way we living beings do).

deleted 1y ago
[deleted]
travisjungroth 1y ago
Copied from a past comment of mine: I just made up this scenario and these words, so I'm sure it wasn't in the training data. Kwomps can zark but they can't plimf. Ghirns are a lot like Kwomps, but better zarkers. Plyzers have the skills the Ghirns lack. Quoning, a type of plimfing, was developed in 3985. Zhuning was developed 100 years earlier. I have an erork that needs to be plimfed. Choose one group and one method to do it. > Use Plyzers and do a Quoning procedure on your erork. If that doesn't count as reasoning or generalization, I don't know what does.
- firesteelrain 1y ago
  It’s just a truth table. I had a hunch that it was a truth table and then I asked AI how it figured it out and it confirmed it built a truth table. Still impressive either way * Goal: Pick (Group ∧ Method) such that Group can plimf ∧ Method is a type of plimfing * Only one group (Plyzers) passes the "can plimf" test * Only one method (Quoning) is definitely plimfing Therefore, the only valid (Group ∧ Method) combo is: → (Plyzer ∧ Quoning) Source: ChatGPT
  - travisjungroth 1y ago
    So? Is the standard now that reasoning using truth tables or reasoning that can be expressed as truth tables doesn’t count?
    - krackers 1y ago
      If anything you'd think that the neurosymbolic people would be pleased that the LLMs do in fact reason by learning circuits representing boolean logic and truth tables. In a way they were right, it's just that starting with logic and then feeding in knowledge grounded in that logic (like Cyc) seems less scalable than feeding in knowledge and letting the model infer the underlying logic.
      - firesteelrain 1y ago
        Right, that’s my point. LLMs are doing pattern abstraction and in this way can mimic logic. They are not trained explicitly to do just truth tables even thought truth tables are fundamental.
socalgal2 1y ago
I'm working on new API. I asked the LLM to read the spec and write tests for it. It does. I don't know if that's "reasoning". I know that no tests exist for this API. I know that the internet is not full of training data for this API because it's a new API. It's also not a CRUD API or some other API that's got a common pattern. And yet, with a very short prompt, Gemini Code Assist wrote valid tests for a new feature. It certainly feels like more than fancy auto-complete. That is not to say I haven't run into issue but I'm still often shocked at how far it gets. And that's today. I have no idea what to expect in 6 months, 12, 2 years, 4, etc.
- godelski 1y ago
  > I know that the internet is not full of training data for this API because it's a new API. 1) are you sure? That's a bold guess. It was also a really stupid assumption made by the HumanEval benchmark authors. That if you "hand write" simple leet code style questions then you can train on all of GitHub. Go ahead, go look at what kinds of questions are in that benchmark... 2) LLMs aren't discrete databases. They are curve fitting functions. Compression. They work in very very high dimensions. They can generate new data but that is limited. People mostly aren't saying that LLMs can't create novel things but that they can't reason in the way that humans can. Humans can't memorize half of what a LLM can yet are able to figure out lots of crazy shit.

deleted 1y ago

[deleted]

travisjungroth 1y ago

Copied from a past comment of mine: I just made up this scenario and these words, so I'm sure it wasn't in the training data. Kwomps can zark but they can't plimf. Ghirns are a lot like Kwomps, but better zarkers. Plyzers have the skills the Ghirns lack. Quoning, a type of plimfing, was developed in 3985. Zhuning was developed 100 years earlier. I have an erork that needs to be plimfed. Choose one group and one method to do it. > Use Plyzers and do a Quoning procedure on your erork. If that doesn't count as reasoning or generalization, I don't know what does.

firesteelrain 1y ago
It’s just a truth table. I had a hunch that it was a truth table and then I asked AI how it figured it out and it confirmed it built a truth table. Still impressive either way * Goal: Pick (Group ∧ Method) such that Group can plimf ∧ Method is a type of plimfing * Only one group (Plyzers) passes the "can plimf" test * Only one method (Quoning) is definitely plimfing Therefore, the only valid (Group ∧ Method) combo is: → (Plyzer ∧ Quoning) Source: ChatGPT
- travisjungroth 1y ago
  So? Is the standard now that reasoning using truth tables or reasoning that can be expressed as truth tables doesn’t count?
  - krackers 1y ago
    If anything you'd think that the neurosymbolic people would be pleased that the LLMs do in fact reason by learning circuits representing boolean logic and truth tables. In a way they were right, it's just that starting with logic and then feeding in knowledge grounded in that logic (like Cyc) seems less scalable than feeding in knowledge and letting the model infer the underlying logic.
    - firesteelrain 1y ago
      Right, that’s my point. LLMs are doing pattern abstraction and in this way can mimic logic. They are not trained explicitly to do just truth tables even thought truth tables are fundamental.

socalgal2 1y ago

I'm working on new API. I asked the LLM to read the spec and write tests for it. It does. I don't know if that's "reasoning". I know that no tests exist for this API. I know that the internet is not full of training data for this API because it's a new API. It's also not a CRUD API or some other API that's got a common pattern. And yet, with a very short prompt, Gemini Code Assist wrote valid tests for a new feature. It certainly feels like more than fancy auto-complete. That is not to say I haven't run into issue but I'm still often shocked at how far it gets. And that's today. I have no idea what to expect in 6 months, 12, 2 years, 4, etc.

godelski 1y ago
> I know that the internet is not full of training data for this API because it's a new API. 1) are you sure? That's a bold guess. It was also a really stupid assumption made by the HumanEval benchmark authors. That if you "hand write" simple leet code style questions then you can train on all of GitHub. Go ahead, go look at what kinds of questions are in that benchmark... 2) LLMs aren't discrete databases. They are curve fitting functions. Compression. They work in very very high dimensions. They can generate new data but that is limited. People mostly aren't saying that LLMs can't create novel things but that they can't reason in the way that humans can. Humans can't memorize half of what a LLM can yet are able to figure out lots of crazy shit.