• @UnseriousAcademic@awful.systems
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    147 months ago

    Does this mean they’re not going to bother training a whole new model again? I was looking forward to seeing AI Mad Cow Disease after it consumed an Internet’s worth of AI generated content.

    • @anton@lemmy.blahaj.zone
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      87 months ago

      If you change the tokenizer you have to retrain from scratch, but you can do so with the old, unpolluted data.

      It’s genius if you think about it,* you can waste energy and tell your investors it’s a new better model, while staying upstream from the river you pollute.
      * at least for consultants, compute providers and other middle men.

      • @UnseriousAcademic@awful.systems
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        47 months ago

        I remember one time in a research project I switched out the tokeniser to see what impact it might have on my output. Spent about a day re-running and the difference was minimal. I imagine it’s wholly the same thing.

        *Disclaimer: I don’t actually imagine it is wholly the same thing.

        • David GerardOPM
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          47 months ago

          there’s a research result that the precise tokeniser makes bugger all difference, it’s almost entirely the data you put in

          because LLMs are lossy compression for text