[baranul]

This is what I have observed as well. Been through many debates about the "perfect plan" and "perfect tests" fallacies.

Maybe a way of looking at it, to understand the nature of the issue. Have a LLM translate a novel from English to Spanish. Of course it can do that translation at speeds that no human could (score a point for AI). But how good is the Spanish translation? Is the quality better than what humans could do? Wouldn't those who are not fluent in Spanish be more easily impressed?

We then can do all kinds of configuration setups and tests, but how do we know the Spanish was translated perfectly, without a massive detail review (and being already truly bilingual in both English and Spanish)?

As is the usual case in the pursuit of perfection (which nothing in nature ever seems to be), there is going to be mistakes, costs (worth it?), and gray areas. It would be foolhardy for us not to suspect or pass it off as otherwise.

[deanishe]

> how do we know the Spanish was translated perfectly, without a massive detail review

You can't. I think that's a large part of why LLMs have caught on much better with programmers: they have ways of making the computer check its own work.

Checking a document is still a laborious manual task. And completely unfulfilling.

[baranul]

And that's the trap. Relying on a "perfectly" crafted test or a LLM to verify what is unknown. Yes, LLMs can be very useful, but perhaps it's best for us to be realistic.

> making the computer check its own work

Kind of like the Spanish teacher telling his students they can grade their own tests, then being surprised that Billy was always giving himself 100%, when he's nowhere near that bright or fluent.

It wouldn't be so bad, if people were more upfront with being unsure or made it clear they were extrapolating from smaller and limited data. But usually, like many of these unusually cocky LLMs, what is too often reported to the public is "perfection" and many inconvenient truths "swept underneath the carpet".