this post was submitted on 12 Sep 2023
146 points (100.0% liked)
Technology
37353 readers
277 users here now
Rumors, happenings, and innovations in the technology sphere. If it's technological news or discussion of technology, it probably belongs here.
Subcommunities on Beehaw:
This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
They're both BS machines and fact generators. It produced bullshit when asked about him because as far as I can tell he's kind of a nobody, not because it's just a stylistic generator. If he asked about a more prominent person likely to exist more significantly within the training corpus, it would likely be largely accurate. The hallucination problem stems from the system needing to produce a result regardless of whether it has a well trained semantic model for the question.
LLMs encode both the style of language and semantic relationships. For "who is Einstein", both paths are well developed and the result is a reasonable response. For "who is Ryan McGreal", the semantic relationships are weak or non-existent, but the stylistic path is undeterred, leading to the confidently plausible bullshit.
They don't generate facts, as the article says. They choose the next most likely word. Everything is confidently plausible bullshit. That some of it is also true is just luck.
It's obviously not "just" luck. We know LLMs learn a variety of semantic models of varying degrees of correctness. It's just that no individual (inner) model is really that great, and most of them are bad. LLMs aren't reliable or predictable (enough) to constitute a human-trustable source of information, but they're not pure gibberish generators.
No, it's true, "luck" might be overstating it. There's a good chance most of what it says is as accurate as the corpus it was trained on. That doesn't personally make me very confident, but ymmv.