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marco o1 at home: https://www.reddit.com/r/LocalLLaMA/comments/1gyx1hj/macroo1_opensource_o1_gives_the_cutest_ai/ https://www.reddit.com/r/LocalLLaMA/comments/1

marco o1 at home: https://www.reddit.com/r/LocalLLaMA/comments/1gyx1hj/macroo1_opensource_o1_gives_the_cutest_ai/ https://www.reddit.com/r/LocalLLaMA/comments/1gyx1hj/macroo1...

TeMPOraL 2y ago
That's... a good result, actually. No, I'm serious. This reads exactly like my inner thought process on a novel or tricky task I'm asked to solve, especially when I know I'm tired (or drunk, back in the times I consumed alcohol on a regular basis), and need to spell everything out (out loud or in a text file). Hell, it's exactly how I expect a kid who just learned about fractions would think. I have a vague recollection I processed such tasks this explicitly as a kid, until I understood the topic. LLMs pulling this off reliably? That's huge progress. I used to say[0] that GPT-4 is best imagined as a 4 year old kid that memorized half the Internet. But this? This is 8 year old's stuff. -- [0] - I currently prefer comparing it to "inner voice", and its performance and propensity to hallucinations to a smart schoolkid that's being asked questions by the teacher about things they only read about but didn't fully process, and who's pressured into giving some answer, as saying "I don't know" is an instant F and public humiliation. Such kid will be forced to extrapolate on the spot, but if they're smart enough and remember enough, they'll often get it at least partially right. I know that from personal experience :).
- caturopath 2y ago
  Yeah, I don't know if someone thought this was bad or something, but it seems like valid reasoning. We may need to give these models a sense of things that require more and less detailed reasoning and better knowledge of what they know internally, but the "research work" the model claims to be, it seems like it's doing a good job. The poster also shared in a comment https://preview.redd.it/u8vs29hq5w2e1.png?width=2704&format=png&auto=webp&s=b88a6d33128a7f18f8ff11fa9bafd0bf095e56c3 https://preview.redd.it/u8vs29hq5w2e1.png?width=2704&format=... which did get the intended laugh out of me, but even that seems fair enough. I'm currently traveling in a country where most people speak a language I don't know well. You better believe I've been thinking through even trivial greetings, considering the setting, formality, appropriate follow ups, etc.
  - behnamoh 2y ago
    OP here. I liked Macro-o1 (Marco1?) but as you pointed out, we need to teach these models to spend their system 2 thinking more economically.
    - caturopath 2y ago
      One of the things complicating the example is that the counting-letters task is actually a legitimately hard one for it, even if it's trivial for us: it's bad at letter stuff because of the way it represents text with tokens. I believe the problem exists, but the letter thing isn't necessarily a great example of something that it should recognize as trivial.
  - TeMPOraL 2y ago
    Looking at that example, I too feel it's a thought process I could go through once or twice. I think this highlights an important difference between humans and LLMs: a human can think such things explicitly in their head, or on paper (I do it in text editor more frequently than I'd care to admit), once or twice, and then it sticks, quickly becomes more of a "system 1" thing. With LLMs, the closest to that outside training/fine-tuning would probably be prompt caching. It would be great if we could figure out some kind of on-line learning scheme so the model could internalize its own thoughts and persist them between conversations, but in the latent space, not prepended as token input.
  - caturopath 2y ago
    > ou better believe I've been thinking through even trivial greetings Even after thinking through what to say, I used the wrong greeting in a shop half an hour ago and the person working there called me on it.

marco o1 at home: https://www.reddit.com/r/LocalLLaMA/comments/1gyx1hj/macroo1_opensource_o1_gives_the_cutest_ai/ https://www.reddit.com/r/LocalLLaMA/comments/1gyx1hj/macroo1...

TeMPOraL 2y ago

That's... a good result, actually. No, I'm serious. This reads exactly like my inner thought process on a novel or tricky task I'm asked to solve, especially when I know I'm tired (or drunk, back in the times I consumed alcohol on a regular basis), and need to spell everything out (out loud or in a text file). Hell, it's exactly how I expect a kid who just learned about fractions would think. I have a vague recollection I processed such tasks this explicitly as a kid, until I understood the topic. LLMs pulling this off reliably? That's huge progress. I used to say[0] that GPT-4 is best imagined as a 4 year old kid that memorized half the Internet. But this? This is 8 year old's stuff. -- [0] - I currently prefer comparing it to "inner voice", and its performance and propensity to hallucinations to a smart schoolkid that's being asked questions by the teacher about things they only read about but didn't fully process, and who's pressured into giving some answer, as saying "I don't know" is an instant F and public humiliation. Such kid will be forced to extrapolate on the spot, but if they're smart enough and remember enough, they'll often get it at least partially right. I know that from personal experience :).

caturopath 2y ago
Yeah, I don't know if someone thought this was bad or something, but it seems like valid reasoning. We may need to give these models a sense of things that require more and less detailed reasoning and better knowledge of what they know internally, but the "research work" the model claims to be, it seems like it's doing a good job. The poster also shared in a comment https://preview.redd.it/u8vs29hq5w2e1.png?width=2704&format=png&auto=webp&s=b88a6d33128a7f18f8ff11fa9bafd0bf095e56c3 https://preview.redd.it/u8vs29hq5w2e1.png?width=2704&format=... which did get the intended laugh out of me, but even that seems fair enough. I'm currently traveling in a country where most people speak a language I don't know well. You better believe I've been thinking through even trivial greetings, considering the setting, formality, appropriate follow ups, etc.
- behnamoh 2y ago
  OP here. I liked Macro-o1 (Marco1?) but as you pointed out, we need to teach these models to spend their system 2 thinking more economically.
  - caturopath 2y ago
    One of the things complicating the example is that the counting-letters task is actually a legitimately hard one for it, even if it's trivial for us: it's bad at letter stuff because of the way it represents text with tokens. I believe the problem exists, but the letter thing isn't necessarily a great example of something that it should recognize as trivial.
- TeMPOraL 2y ago
  Looking at that example, I too feel it's a thought process I could go through once or twice. I think this highlights an important difference between humans and LLMs: a human can think such things explicitly in their head, or on paper (I do it in text editor more frequently than I'd care to admit), once or twice, and then it sticks, quickly becomes more of a "system 1" thing. With LLMs, the closest to that outside training/fine-tuning would probably be prompt caching. It would be great if we could figure out some kind of on-line learning scheme so the model could internalize its own thoughts and persist them between conversations, but in the latent space, not prepended as token input.
- caturopath 2y ago
  > ou better believe I've been thinking through even trivial greetings Even after thinking through what to say, I used the wrong greeting in a shop half an hour ago and the person working there called me on it.