Comment on OpenAI strikes Reddit deal to train its AI on your posts

• Ghostalmedia@lemmy.world ⁨8⁩ ⁨months⁩ ago

  A model trained on jokes about bacon, narwhals, and rage comics.

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  • FaceDeer@fedia.io ⁨8⁩ ⁨months⁩ ago

    By "old archives" I mean everything from 2022 and earlier.

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    • BakerBagel@midwest.social ⁨8⁩ ⁨months⁩ ago

      But there were still bots making shit up back then. r/SubredditSimulator was pretty popular for awhile, and repost and astroturfing bots were a problem form decades on Reddit.

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      • FaceDeer@fedia.io ⁨8⁩ ⁨months⁩ ago

        Existing AIs such as ChatGPT were trained in part on that data so obviously they've got ways to make it work. They filtered out some stuff, for example - the "glitch tokens" such as solidgoldmagikarp were evidence of that.

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    • Ghostalmedia@lemmy.world ⁨8⁩ ⁨months⁩ ago

      I SAID RAGE COMICS

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• mint_tamas@lemmy.world ⁨8⁩ ⁨months⁩ ago

  That paper is yet to be peer reviewed or released. I think you are jumping into conclusion with that statement. How much can you dilute the data until it breaks again?

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  • barsoap@lemm.ee ⁨8⁩ ⁨months⁩ ago

    That paper is yet to be peer reviewed or released.

    Never doing either (release as in submit to journal) isn’t uncommon in maths, physics, and CS. Not to say that it won’t be released but it’s not a proper standard to measure papers by.

    I think you are jumping into conclusion with that statement. How much can you dilute the data until it breaks again?

    Quoth:

    If each linear model is instead fit to the generate targets of all the preceding linear models i.e. data accumulate, then the test squared error has a finite upper bound, independent of the number of iterations. This suggests that data accumulation might be a robust solution for mitigating model collapse.

    Emphasis on “finite upper bound, independent of the number of iterations” by doing nothing more than keeping the non-synthetic data around each time you ingest new synthetic data. This is an empirical study so of course it’s not proof you’ll have to wait for theorists to have their turn for that one, but it’s darn convincing and should henceforth be the null hypothesis.

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    • mint_tamas@lemmy.world ⁨8⁩ ⁨months⁩ ago

      Peer review, for all its flaws is a good minimum before a paper is worth taking seriously.

      In your original comment you said tha model collapse can be easily avoided with this technique, which is notably different from it being mitigated. I’m not saying that these findings are not useful, just that you are overselling them a bit with this wording.

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      • barsoap@lemm.ee ⁨8⁩ ⁨months⁩ ago

        It was someone different who said that. There’s a chance the authors might’ve gotten some claim wrong because their maths and/or methodology is shoddy but it’s a large and diverse set of authors so that’s unlikely. Fraud in CS empirics is generally unheard of, I mean what are you going to do when challenged, claim that the dog ate the program you ran to generate the data? There’s shenanigans about the equivalent of p-hacking especially from papers from commercial actors trying to sell stuff but that’s not the case here, either.

        CS academics generally submit papers to journals more because of publish or perish than the additional value formal peer review offers. It’s on the internet, after all. By all means, if you spot something in the paper that’s wrong then be right on the internet.

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