Indeed, & an artificial uniformity of LLMs, and thought, if everyone is cribbing each others' outputs could be a concern.
But before its concern about a monoculture, the article 1st points out that mere prediction-training (on another model's outputs or fresh data) can't truly match RLHF in instilling some much-desired behaviors.
And that presents a bit of a tension with the article's 2nd concern: if mere output-mimicking *can't* match more-sophisticated training, then it can't really create the concerning uniformity, either.
And maybe: the dose makes the poison. A little cribbing might be a beneficial partial accelerator for smaller teams & newer projects, even if a lot is ineffective (at ever fully replicating OpenAI model behaviors), or deleterious (if effective and also overdone).
So the article isn't really a strong case for not trying this at all – just for keeping the potential limits & downsides in mind, in any experiments with this technique.
But before its concern about a monoculture, the article 1st points out that mere prediction-training (on another model's outputs or fresh data) can't truly match RLHF in instilling some much-desired behaviors.
And that presents a bit of a tension with the article's 2nd concern: if mere output-mimicking *can't* match more-sophisticated training, then it can't really create the concerning uniformity, either.
And maybe: the dose makes the poison. A little cribbing might be a beneficial partial accelerator for smaller teams & newer projects, even if a lot is ineffective (at ever fully replicating OpenAI model behaviors), or deleterious (if effective and also overdone).
So the article isn't really a strong case for not trying this at all – just for keeping the potential limits & downsides in mind, in any experiments with this technique.