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wood_spirit | on: LLMs work best when the user defines their acceptance criteria first
Sorry I can think of so many counter examples. I also detect a lot of “well it hallucinates about subject X (that the person knows well, so can spot the hallucination)” but continue to trust it on subjects Y and Z (which the person knows less well so can’t spot the hallucinations).

YMMV.

andai | parent | next
> Briefly stated, the Gell-Mann Amnesia effect works as follows. You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward-reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them. In any case, you read with exasperation or amusement the multiple errors in a story-and then turn the page to national or international affairs, and read with renewed interest as if the rest of the newspaper was somehow more accurate about far-off Palestine than it was about the story you just read. You turn the page, and forget what you know.

-Michael Crichton

satvikpendem | parent
Sure, Gell-Mann amnesia exists, but remember that its origin is actually human, in the form of newspaper writers. So, how can we trust humans the same way? In just the same way, AI cannot also be fully trusted.
wood_spirit | root | parent
The current way of doing AI cannot be trusted.

that doesn’t mean the future won’t herald a way of using what a transformer is good at - interfacing with humans - to translate to and interact with something that can be a lot more sound and objective.

satvikpendem | root | parent
You're falling into the extrapolation fallacy, there is no reason to think that the future won't have the same issues as today in terms of hallucinations.

And even if they were solved, how would that even work? The world is not sound and objective.

wood_spirit | root | parent
It’s a thought experiment. I am not saying I believe it will happen.

But right now there are lots of domains where current lauded success is in treating something objective - like code - as tokens for an llm.

We could instead explore using transformers to translate human languages to a symbology that can be reasoned about and applied eg to code.

It’s the talk of conferences. But whether it works better than we have today, or whether it aligns with the incentives or the big players, is another matter