We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model.

I often see a lot of people with outdated understanding of modern LLMs.

This is probably the best interpretability research to date, by the leading interpretability research team.

It’s worth a read if you want a peek behind the curtain on modern models.

  • tabarnaski@sh.itjust.worksEnglish
    7·
    6 months ago

    I think the most interesting thing in this article is the fact that some concepts central to semantics (analogy, connotation) or psychology (bias) kind of emerge naturally in multi layered neural networks of sufficient size. Also that it can sound like different personalities (overconfident, secretive, delusional) if you manipulate the weight or the proximity of features. I’d like to see the same kind of study but for midjourney…

    • GiveMemes@jlai.luEnglish
      1·
      6 months ago

      That’s a chicken and egg situation tho. Is the bias a result of a mind? Or is it the result of being trained on data with common human biases all put together by humans? Are these traits actually measurable or are we just anthropomorphizing a machine like we do everything else?

    • magic_lobster_party@kbin.run
      1·
      6 months ago

      I would imagine a similar result. Like how the word “cartoon” activates one particular feature. And if you identify this feature you can control the level of “cartooniness” by tweaking the particular feature.