3 ms·

> they are incapable of even the simplest "out-of-distribution" deductive reasoning But the link demonstrates the opposite- these models absolutely are able to

by _yb2s 2y ago

> they are incapable of even the simplest "out-of-distribution" deductive reasoning But the link demonstrates the opposite- these models absolutely are able to reason out of distribution, just not with perfect fidelity. The fact that they can do better than random is itself really impressive. And o1-preview does impressively well, only vary rarely getting the wrong answer on variants of that Alice in Wonderland problem. If you would listen to most of the people critical of LLMs saying they're a "stochastic parrot" - it should be impossible for them to do better than random on any out of distribution problem. Even just changing one number to create a novel math problem should totally stump them and result in entirely random outputs, but it does not. Overall, poor reasoning that is better than random but frequently gives the wrong answer is fundamentally, categorically entirely different from being incapable of reasoning.

danielmarkbruce 2y ago
anyone saying an LLM is a stochastic parrot doesn't understand them... they are just parroting what they heard.
- bloomingkales 2y ago
  There is definitely a mini cult of people that want to be very right about how everyone else is very wrong about AI.
  - danielmarkbruce 2y ago
    ie, the people that AI is dumb? Or you are saying I'm in a cult for being pro it - I'm definitely part of that cult - the "we already have agi and you have to contort yourself into a pretzel to believe otherwise" cult. Not sure if there is a leader though.
    - bloomingkales 2y ago
      I didn't realize my post can be interpreted either way. I'll leave it ambiguous, hah. Place your bets I guess.
    - jamiek88 2y ago
      You think we have AGI? What makes you think that?
      - danielmarkbruce 2y ago
        By knowing what each of the letters stand for
        jamiek88 2y ago
        Well that’s disappointing. It was an extraordinary claim that really interested me. Thought I was about to be learn! Instead, I just met an asshole.
        danielmarkbruce 2y ago
        When someone says "i'm in the cult that believes X", don't expect a water tight argument for the existence of X.
        deleted 2y ago
        [deleted]
  - mlinsey 2y ago
    There are a couple Twitter personalities that definitely fit this description. There is also a much bigger group of people that haven't really tried anything beyond GPT-3.5, which was the best you could get without paying a monthly subscription for a long time. One of the biggest reasons for r1 hype, besides the geopolitical angle, was people could actually try a reasoning model for free for the first time.
  - ggm 2y ago
    Firstly this is meta ad hom. You're ignoring the argument to target the speaker(s) Secondly, you're ignoring the fact that the community of voices with experience in data sciences, computer science and artificial intelligence themselves are split on the qualities or lack of them in current AI. GPT and LLM are very interesting but say little or nothing to me of new theory of mind, or display inductive logic and reasoning, or even meet the bar for a philosophers cave solution to problems. We've been here before so many, many times. "Just a bit more power captain" was very strong in connectionist theories of mind. fMRI brains activity analytics, you name it. So yes. There are a lot of "us" who are pushing back on the hype, and no we're not a mini cult.
    - visarga 2y ago
      > GPT and LLM are very interesting but say little or nothing to me of new theory of mind, or display inductive logic and reasoning, or even meet the bar for a philosophers cave solution to problems. The simple fact they can generate language so well makes me think... maybe language itself carries more weight than we originally thought. LLMs can get to this point without personal experience and embodiment, it should not have been possible, but here we are. I think philosophers are lagging science now. The RL paradigm of agent-environment-reward based learning seems to me a better one than what we have in philiosophy now. And if you look at how LLMs model language as high dimensional embedding spaces .. this could solve many intractable philosophical problems, like the infinite homunculus regress problem. Relational representations straddle the midpoint between 1st and 3rd person, offering a possible path over the hard problem "gap".
- ggm 2y ago
  A good literary production. I would have been proud of it had I thought of it, but it's a path to observe a strong "whataboutery" element that if we use "stochastic parrot" as shorthand and you dislike the term, now you understand why we dislike the constant use of "infer", "reason" and "hallucinate" Parrots are self aware, complex reasoning brains which can solve problems in geometry, tell lies, and act socially or asocially. They also have complex vocal chords and can perform mimicry. Very few aspects of a parrots behaviour are stochastic but that also underplays how complex stochastic systems can be in their production. If we label LLM products as Stochastic Parrots it does not mean they like cuttlefish bones or are demonstrably modelled by Markov chains like Mark V Shaney.
  - gsam 2y ago
    I don't like wading into this debate when semantics are very personal/subjective. But to me, it seems like almost a sleight of hand to add the stochastic part, when actually they're possibly weighted more on the parrot part. Parrots are much more concrete, whereas the term LLM could refer to the general architecture. The question to me seems: If we expand on this architecture (in some direction, compute, size etc.), will we get something much more powerful? Whereas if you give nature more time to iterate on the parrot, you'd probably still end up with a parrot. There's a giant impedance mismatch here (time scaling being one). Unless people want to think of parrots being a subset of all animals, and so 'stochastic animal' is what they mean. But then it's really the difference of 'stochastic human' and 'human'. And I don't think people really want to face that particular distinction.
    - _yb2s 2y ago
      I'm sure both of you know this, but "stochastic parrot" refers to the title of a research article that contained a particular argument about LLM limitations that had very little to do with parrots.
      - danielmarkbruce 2y ago
        The term is much more broadly known than the content of that (rather silly) paper.... I'm not even certain that it's the first use of the term.
Jensson 2y ago
> If you would listen to most of the people critical of LLMs saying they're a "stochastic parrot" - it should be impossible for them to do better than random on any out of distribution problem. Even just changing one number to create a novel math problem should totally stump them and result in entirely random outputs, but it does not. You don't seem to understand how they work, they recurse their solution meaning if they have remembered components it parrots back sub solutions. Its a bit like a natural language computer, that way you can get them to do math etc, although the instruction set isn't of a turing language. They can't recurse sub sub parts they haven't seen, but problems that has similar sub parts can of course be solved, anyone understands that.
- _yb2s 2y ago
  > You don't seem to understand how they work I don't think anyone understands how they work- these type of explanations aren't very complete or accurate. Such explanations/models allow one to reason out what types of things they should be capable of vs incapable of in principle regardless of scale or algorithm tweaks, and those predictions and arguments never match reality and require constant goal post shifting as the models are scaled up. We understand how we brought them about via setting up an optimization problem in a specific way, that isn't the same at all as knowing how they work. I tend to think in the totally abstract philosophical sense, independent of the type of model, at the limit of an increasingly capable function approximator trained on an increasingly large and diverse set of real world cause/effect time series data, you eventually develop and increasingly accurate and general predictive model of reality organically within the model. Some model types do have fundamental limits in their ability to scale like this, but we haven't yet found one with these models. It is more appropriate to objectively test what they can and cannot do, and avoid trying to infer what we expect from how we think they work.
  - codr7 2y ago
    Well we do know pretty much exactly what they do, don't we? What surprises us is the behaviors coming out of that process. But surprise isn't magic, magic shouldn't even be on the list of explanations to consider.

by _yb2s 2y ago

danielmarkbruce 2y ago

anyone saying an LLM is a stochastic parrot doesn't understand them... they are just parroting what they heard.

bloomingkales 2y ago
There is definitely a mini cult of people that want to be very right about how everyone else is very wrong about AI.
- danielmarkbruce 2y ago
  ie, the people that AI is dumb? Or you are saying I'm in a cult for being pro it - I'm definitely part of that cult - the "we already have agi and you have to contort yourself into a pretzel to believe otherwise" cult. Not sure if there is a leader though.
  - bloomingkales 2y ago
    I didn't realize my post can be interpreted either way. I'll leave it ambiguous, hah. Place your bets I guess.
  - jamiek88 2y ago
    You think we have AGI? What makes you think that?
    - danielmarkbruce 2y ago
      By knowing what each of the letters stand for
      - jamiek88 2y ago
        Well that’s disappointing. It was an extraordinary claim that really interested me. Thought I was about to be learn! Instead, I just met an asshole.
        danielmarkbruce 2y ago
        When someone says "i'm in the cult that believes X", don't expect a water tight argument for the existence of X.
        deleted 2y ago
        [deleted]
- mlinsey 2y ago
  There are a couple Twitter personalities that definitely fit this description. There is also a much bigger group of people that haven't really tried anything beyond GPT-3.5, which was the best you could get without paying a monthly subscription for a long time. One of the biggest reasons for r1 hype, besides the geopolitical angle, was people could actually try a reasoning model for free for the first time.
- ggm 2y ago
  Firstly this is meta ad hom. You're ignoring the argument to target the speaker(s) Secondly, you're ignoring the fact that the community of voices with experience in data sciences, computer science and artificial intelligence themselves are split on the qualities or lack of them in current AI. GPT and LLM are very interesting but say little or nothing to me of new theory of mind, or display inductive logic and reasoning, or even meet the bar for a philosophers cave solution to problems. We've been here before so many, many times. "Just a bit more power captain" was very strong in connectionist theories of mind. fMRI brains activity analytics, you name it. So yes. There are a lot of "us" who are pushing back on the hype, and no we're not a mini cult.
  - visarga 2y ago
    > GPT and LLM are very interesting but say little or nothing to me of new theory of mind, or display inductive logic and reasoning, or even meet the bar for a philosophers cave solution to problems. The simple fact they can generate language so well makes me think... maybe language itself carries more weight than we originally thought. LLMs can get to this point without personal experience and embodiment, it should not have been possible, but here we are. I think philosophers are lagging science now. The RL paradigm of agent-environment-reward based learning seems to me a better one than what we have in philiosophy now. And if you look at how LLMs model language as high dimensional embedding spaces .. this could solve many intractable philosophical problems, like the infinite homunculus regress problem. Relational representations straddle the midpoint between 1st and 3rd person, offering a possible path over the hard problem "gap".
ggm 2y ago
A good literary production. I would have been proud of it had I thought of it, but it's a path to observe a strong "whataboutery" element that if we use "stochastic parrot" as shorthand and you dislike the term, now you understand why we dislike the constant use of "infer", "reason" and "hallucinate" Parrots are self aware, complex reasoning brains which can solve problems in geometry, tell lies, and act socially or asocially. They also have complex vocal chords and can perform mimicry. Very few aspects of a parrots behaviour are stochastic but that also underplays how complex stochastic systems can be in their production. If we label LLM products as Stochastic Parrots it does not mean they like cuttlefish bones or are demonstrably modelled by Markov chains like Mark V Shaney.
- gsam 2y ago
  I don't like wading into this debate when semantics are very personal/subjective. But to me, it seems like almost a sleight of hand to add the stochastic part, when actually they're possibly weighted more on the parrot part. Parrots are much more concrete, whereas the term LLM could refer to the general architecture. The question to me seems: If we expand on this architecture (in some direction, compute, size etc.), will we get something much more powerful? Whereas if you give nature more time to iterate on the parrot, you'd probably still end up with a parrot. There's a giant impedance mismatch here (time scaling being one). Unless people want to think of parrots being a subset of all animals, and so 'stochastic animal' is what they mean. But then it's really the difference of 'stochastic human' and 'human'. And I don't think people really want to face that particular distinction.
  - _yb2s 2y ago
    I'm sure both of you know this, but "stochastic parrot" refers to the title of a research article that contained a particular argument about LLM limitations that had very little to do with parrots.
    - danielmarkbruce 2y ago
      The term is much more broadly known than the content of that (rather silly) paper.... I'm not even certain that it's the first use of the term.

Jensson 2y ago

> If you would listen to most of the people critical of LLMs saying they're a "stochastic parrot" - it should be impossible for them to do better than random on any out of distribution problem. Even just changing one number to create a novel math problem should totally stump them and result in entirely random outputs, but it does not. You don't seem to understand how they work, they recurse their solution meaning if they have remembered components it parrots back sub solutions. Its a bit like a natural language computer, that way you can get them to do math etc, although the instruction set isn't of a turing language. They can't recurse sub sub parts they haven't seen, but problems that has similar sub parts can of course be solved, anyone understands that.

_yb2s 2y ago
> You don't seem to understand how they work I don't think anyone understands how they work- these type of explanations aren't very complete or accurate. Such explanations/models allow one to reason out what types of things they should be capable of vs incapable of in principle regardless of scale or algorithm tweaks, and those predictions and arguments never match reality and require constant goal post shifting as the models are scaled up. We understand how we brought them about via setting up an optimization problem in a specific way, that isn't the same at all as knowing how they work. I tend to think in the totally abstract philosophical sense, independent of the type of model, at the limit of an increasingly capable function approximator trained on an increasingly large and diverse set of real world cause/effect time series data, you eventually develop and increasingly accurate and general predictive model of reality organically within the model. Some model types do have fundamental limits in their ability to scale like this, but we haven't yet found one with these models. It is more appropriate to objectively test what they can and cannot do, and avoid trying to infer what we expect from how we think they work.
- codr7 2y ago
  Well we do know pretty much exactly what they do, don't we? What surprises us is the behaviors coming out of that process. But surprise isn't magic, magic shouldn't even be on the list of explanations to consider.