• V0ldek@awful.systems
    link
    fedilink
    English
    arrow-up
    9
    ·
    3 个月前

    I was thinking about this after reading the P(Dumb) post.

    All normal ML applications have a notion of evalutaion, e.g. the 2x2 table of {false,true}x{positive,negative}, or for clustering algorithms some metric of “goodness of fit”. If you have that you can make an experiment that has quantifiable results, and then you can do actual science.

    I don’t even know what the equivalent for LLMs is. I don’t really have time to spare to dig through the papers, but like, how do they do this? What’s their experimental evaluation? I don’t seen an easy way to classify LLM outputs into anything really.

    The only way to do science is hypothesis->experiment->analysis. So how the fuck do the LLM people do this?

    • o7___o7@awful.systems
      link
      fedilink
      English
      arrow-up
      7
      ·
      edit-2
      3 个月前

      Right? “AI” is great if you want to sort a few million images of galaxies into their various morphological classifications and have it done before the end of the decade. A++, good job, no notes.

      You can’t grift off of that very easily, though.

    • self@awful.systems
      link
      fedilink
      English
      arrow-up
      6
      ·
      3 个月前

      I’d really like to know too, especially given how many times we’ve already seen LLMs misused in scientific settings. it’s starting to feel like the LLM people don’t have that notion — but that’s crazy, right?