Need to let loose a primal scream without collecting footnotes first? Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid: Welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.

Any awful.systems sub may be subsneered in this subthread, techtakes or no.

If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.

The post Xitter web has spawned soo many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)

Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.

(Semi-obligatory thanks to @dgerard for starting this - gonna try posting last week’s thread a different way this time)

    • khalid_salad@awful.systemsEnglish
      11·
      19 天前

      The first half was OK, but then they cited this paper.

      LLMs encode much more information about truthfulness than previously recognized. We first discover that the truthfulness information is concentrated in specific tokens, and leveraging this property significantly enhances error detection performance. Yet, we show that such error detectors fail to generalize across datasets, implying that—contrary to prior claims—truthfulness encoding is not universal but rather multifaceted.

      I haven’t read the paper, and probably won’t, but what the shit is this?

      • sc_griffith@awful.systemsEnglish
        15·
        19 天前

        gave it a quick skim. I lack any relevant background. the bit they push most seems to be that you can improve the performance of error detection tools by determining the most important tokens in an answer and running your tools on the tokens near those. this seems to be in contrast to absurdly naive approaches like averaging the tokens (???) or just looking at the last token of the response (???).

        what are the most important tokens? they’re the ones that change the factuality of the answer if you change them. how do you determine that? you don’t, lmao. you just ask an LLM what the most important words are

        what are the error detection tools? you will never guess

        • YourNetworkIsHaunted@awful.systemsEnglish
          11·
          19 天前

          It turns out if you can just make the machine know what the truth is and say that you don’t get hallucinations. Unfortunately the truth isn’t emergent from pure language models and expressing Truth through language alone has been something challenging the human race since Krog try to teach Torg how make stick but pointy.

      • o7___o7@awful.systemsEnglish
        9·
        19 天前

        tired: checking incoming packets for the evil bit

        wired: checking LLM outputs for the truthfulness bit