467
AI Companies Are Buying Tons of Old Books Because They're Free of AI Slop (www.404media.co)
submitted 3 months ago by to c/technology@lemmy.world
103 comments fedilink hide all child comments
you are viewing a single comment's thread
view the rest of the comments
[–] 0 points 3 months ago (4 children)

That's like saying, "they had some failure modes from the synthetic data, so they should just obviously stop trying forever."

They'll just fix the edge cases and move on. Like any programming task.

  • permalink
  • fedilink
  • source
  • parent
  • hideshow 4 child comments
    •
  • [–] 3 points 3 months ago* (last edited 3 months ago) (3 children)

    The problem is that synthetic data is not fit for that purpose. The more of it you use, the worse at dealing with the edge cases LLMs get.

    Think of it like this... You feed a language model a bunch of genuine human-written content. Great. Now it can produce the most likely text in a lot of cases. Word combinations that rarely appear in written language rarely get generated, so most of its synthetic data lacks those rare - but still valid - combinations.

    Train it on this synthetic data, and now more outliers and rare combinations get filed off. Rinse and repeat.

  • permalink
  • fedilink
  • source
  • parent
  • hideshow 3 child comments
    •
  • [–] 1 point 3 months ago (1 child)

    I think what they're saying is that the AI companies don't need to rinse and repeat. They've already created solid programs for how to make LLMS speak English and demonstrate basic reasoning. You don't need to retrain that part every time. Once you've trained "English.exe," you can just copy it endlessly.

    Maybe with enough time, the hard-coded English of the LLMs could become increasingly anachronistic and sound old-fashioned and formal to most ears. But for that kind of for slow maintenance you could just pay people to write examples of modern language and train it on that.

  • permalink
  • fedilink
  • source
  • parent
  • hideshow 1 child comment
    •
  • [–] 1 point 3 months ago

    Then that's a misunderstanding on their part. What I'm getting at is that if all of your new training data doesn't reinforce uncommon - but factually and grammatically correct - outlier word relationships, then those outliers fade away.

    There's also an amplification issue. OpenAI has had to add a ton of instructions to their harnesses not to mention goblins because the model trained on a bunch of synthetic data when one of ChatGPT's offered personalities would go "goblin mode." So the more it mentioned goblins, the more that data was accidentally fed back into it, and all of a sudden no matter which personality you assigned, ChatGPT would go off about goblins.

  • permalink
  • fedilink
  • source
  • parent