>People also tend not to understand the absurdity of assuming that we can make LLMs stop hallucinating. It would imply not only that truth is absolutely objective, but that it exists on some smooth manifold which language can be mapped to.

Frankly, this is a silly line of argument. There is a vast spectrum between regularly inventing non-existent citations and total omniscience. "We can't define objective truth" isn't a gotcha, it's just irrelevant.

Nobody in the field is talking about or working on completely eliminating hallucinations in some grand philosophical sense, they're just grinding away at making the error rate go down, because that makes models more useful. As shown in this article, relatively simple changes can have a huge effect and meaningful progress is being made very rapidly.

We've been here before, with scepticism about Wikipedia. A generation of teachers taught their students "you can't trust Wikipedia, because anyone can edit it". Two decades and a raft of studies later, it became clear that Wikipedia is at least as factually accurate as traditional encyclopedias and textbooks. The contemporary debate about the reliability of Wikipedia is now fundamentally the same as arguments about the reliability of any carefully-edited resource, revolving around subtle and insidious biases rather than blatant falsehoods.

Large neural networks do not have to be omniscient to be demonstrably more reliable than all other sources of knowledge, they just need to keep improving at their current rate for a few more years. Theoretical nitpicking is missing the forest for the trees - what we can empirically observe about the progress in AI development should have us bracing ourselves for radical social and economic transformation.

You're not being charitable with the take. seems like you just switched "objective truth" with your flavor: "error rate"

what is an error? how does the llm "know"?

wikipedia example is good. i'd say its "truth" is based on human curated consensus. everyone gets that. what i don't get what's the llm analog? as you state, it's just about making the error rate go down, ok so what is an error? does it require human in the loop?

The thing is, for a lot of tasks, a formal method (either algorithmic or simulation) can be very efficient to create and run with more reliable results. And for a lot of cases, creating a simpler and smaller model with other ML techniques can be as good or better than LLMs.

There's still no justification for the whole investment craze in LLMs.

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