4 ms·

>the output is conditional on the input Uh, isn't that how it should always be? If the output isn't conditional on the input, then it is basically noise or wor

>the output is conditional on the input Uh, isn't that how it should always be? If the output isn't conditional on the input, then it is basically noise or worthless. How the conditionals are setup are the key but not really sure how does this relate to the point you are making.

kgwgk 4y ago
According to the parent comment anyone who says "LLMs are stochastic parrots" is ignorant of the effect that changing the input “filling the context with examples” has on the output.
- nl 4y ago
  That isn't a fair summary of what I'm saying. People who say "LLMs are stochastic parrots" may well be aware of effects of conditioning the input. This in itself does not completely describe the capabilities of a LLM, since their ability to learn and use those new facts to override their previous "beliefs" is not what you'd expect from mere stochastic behavior. My point is that "stochastic parrot" believers are unaware of this.
  - int_19h 4y ago
    They are aware. The thing about the "stochastic parrot" argument is that it can be pushed as far as one is willing to push it; any behavior by an LLM can be described in those terms with enough handwaving. The practical limit seems to be where one would have to say that humans are also "stochastic parrots", presumably because that defeats the point of making such an argument in the first place.
    - mdp2021 4y ago
      You are completely disregarding fairness of judgement based on noted output. Roger was called a parrot because it repeated things apparently without checking; humans are not necessarily parrots, as some do not do that and reflect as expected.
      - int_19h 4y ago
        Quite the opposite - I'm questioning fairness of judgment based on output from GPT-4 when solving complicated tasks etc, which is very hard to justify as "stochastic" unless you beg the question.
        mdp2021 3y ago
        But the judgement is not relevant to a context of "«handwaving»" (BTW: nice concept). If you look at the "handwaving" it becomes a strawman to lose the focus on the objective. If an engine outputs things like "Charles III is the current King of Britain. He was born in January 26, 1763" , it seems to be conflating different notions without having used the necessary logic required for vetting the statement: the witness has reasons to say "this seems like stochastic parroting" - two memories are joined through an accidental link that slightly increases Bayesian values. That appears without need of handwaving. And a well developed human intellect will instead go "Centuries old?! Mh.", which makes putting engine and human in the same pot a futile claim - "handwaving". Similarly for nice cases to be kept for historic divulgation, such as "You will not fool me: ten kilos of iron and half a kilo of feathers weigh the same". When on the other hand the engine «solv[es] complicated tasks», what we want is to understand why. And we do that because, on the engineering side, we want reliable; and on the side of science, we want an increase of understanding, then knowledge, then again possible application. We want to understand what happens in success cases especially because we see failures (evident parroting counts as failure). And it is engineering, so we want reliable - not just the Key Performance Indicator, but the /Critical/ KPI ("pass|fail") is reliability. So the problem is not that sometimes they do not act like "stochastic parrots" (this is a "problem" in a different sense, theoretical), but that they even only sometimes do act like "stochastic parrots". You do not want an FPU that steers astray. Normal practice should be to structure automated tests and see what works, what does not, and assess why.
        int_19h 3y ago
        That is a completely different issue, though. We were not talking about the usefulness of the models' behavior, but rather of its fundamental nature. Right now I wouldn't trust an LLM for anything where the result is not either for entertainment purposes or undergoes human vetting. But I also don't trust many humans, and for reasons that are fundamentally the same - you implicitly acknowledged it by talking about "well-developed human intellect". And I would still trust a random human more than GPT-4 - but, again, this has more to do with its ability to reason being inherently limited (by model size, context size etc), not because it's originally trained to predict tokens.

kgwgk 4y ago

According to the parent comment anyone who says "LLMs are stochastic parrots" is ignorant of the effect that changing the input “filling the context with examples” has on the output.

nl 4y ago
That isn't a fair summary of what I'm saying. People who say "LLMs are stochastic parrots" may well be aware of effects of conditioning the input. This in itself does not completely describe the capabilities of a LLM, since their ability to learn and use those new facts to override their previous "beliefs" is not what you'd expect from mere stochastic behavior. My point is that "stochastic parrot" believers are unaware of this.
- int_19h 4y ago
  They are aware. The thing about the "stochastic parrot" argument is that it can be pushed as far as one is willing to push it; any behavior by an LLM can be described in those terms with enough handwaving. The practical limit seems to be where one would have to say that humans are also "stochastic parrots", presumably because that defeats the point of making such an argument in the first place.
  - mdp2021 4y ago
    You are completely disregarding fairness of judgement based on noted output. Roger was called a parrot because it repeated things apparently without checking; humans are not necessarily parrots, as some do not do that and reflect as expected.
    - int_19h 4y ago
      Quite the opposite - I'm questioning fairness of judgment based on output from GPT-4 when solving complicated tasks etc, which is very hard to justify as "stochastic" unless you beg the question.
      - mdp2021 3y ago
        But the judgement is not relevant to a context of "«handwaving»" (BTW: nice concept). If you look at the "handwaving" it becomes a strawman to lose the focus on the objective. If an engine outputs things like "Charles III is the current King of Britain. He was born in January 26, 1763" , it seems to be conflating different notions without having used the necessary logic required for vetting the statement: the witness has reasons to say "this seems like stochastic parroting" - two memories are joined through an accidental link that slightly increases Bayesian values. That appears without need of handwaving. And a well developed human intellect will instead go "Centuries old?! Mh.", which makes putting engine and human in the same pot a futile claim - "handwaving". Similarly for nice cases to be kept for historic divulgation, such as "You will not fool me: ten kilos of iron and half a kilo of feathers weigh the same". When on the other hand the engine «solv[es] complicated tasks», what we want is to understand why. And we do that because, on the engineering side, we want reliable; and on the side of science, we want an increase of understanding, then knowledge, then again possible application. We want to understand what happens in success cases especially because we see failures (evident parroting counts as failure). And it is engineering, so we want reliable - not just the Key Performance Indicator, but the /Critical/ KPI ("pass|fail") is reliability. So the problem is not that sometimes they do not act like "stochastic parrots" (this is a "problem" in a different sense, theoretical), but that they even only sometimes do act like "stochastic parrots". You do not want an FPU that steers astray. Normal practice should be to structure automated tests and see what works, what does not, and assess why.
        int_19h 3y ago
        That is a completely different issue, though. We were not talking about the usefulness of the models' behavior, but rather of its fundamental nature. Right now I wouldn't trust an LLM for anything where the result is not either for entertainment purposes or undergoes human vetting. But I also don't trust many humans, and for reasons that are fundamentally the same - you implicitly acknowledged it by talking about "well-developed human intellect". And I would still trust a random human more than GPT-4 - but, again, this has more to do with its ability to reason being inherently limited (by model size, context size etc), not because it's originally trained to predict tokens.