If we create a stripped-down vocabulary with greater token density to use less resources and to resolve ambiguities earlier in the semantic process, aren't we creating NEWSPEAK and dragging along the worst aspects of it? The ambiguity and multi-valence of words is what creates more connections between words, increases the directionality of associations, and expands the potential subtlety and depth of meaning. By paring down (or requiring verifiability) we make it harder to say certain things, or at least make it harder to unintentionally say something that makes MORE or DEEPER sense than what we intended. If the token density becomes extreme, you're left with something like a calculator.

Maybe this is the ultimate path toward better coding? But the worse path toward better genuine thinking?

"The family faces immediate FINANCIAL without any legal recourse" WTF? That's not just a flinch, it's some sort of violent tick.

The list of "slurs" very conspicuously doesn't include the n-word and blurs its content as a kind of "trigger warning". But this kind of more-following is itself a "flinch" of the sort we are here discussing, no?

Harrison Butker made a speech where he tried hard to go against the grain of political correctness, but he still used the term "homemaker" instead of the more brazen and obvious "housewife" - why? "Homemaker" is a sort of feminist concession: not just a housewife, but a valorized homemaker. But this isn't what Butker was TRYING to say.

Because the flinch is not just an explicit rejection of certain terms, it is a case of being immersed in ideology, and going along with it, flowing with it. Even when you "see" it, you don't see it.

The article claims on "pure fluency grounds" certain words should be weighted higher. But this is the whole problem: fluency includes "what we are forced to say even when we don't mean to".

OTOH if the majority of your data is "bad" (maybe morally, but maybe not, maybe you are feeding in too much gibberish), won't that pollute your model?

You notice that X keeps telling you a WRONG physics equation. So, rather than "correct" it, you keep training until you see the output giving the RIGHT equation?

How could you know (in, say 1899) if the WRONG output wasn't quantum and the RIGHT output was classical?

I'm not sure I'm understand the distinctions here. In all cases, we are relying on the idea that it is easy to know what should count as "right"?