alt Hacker NewsAn interesting observation from that page:
"Thus the highly specific "inventor of the first train-coupling device" might become "a revolutionary titan of industry." It is like shouting louder and louder that a portrait shows a uniquely important person, while the portrait itself is fading from a sharp photograph into a blurry, generic sketch. The subject becomes simultaneously less specific and more exaggerated."
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To me that seems like we're mistaken in mixing fiction and non-fiction in AI training data. The "a revolutionary titan of industry" makes sense if you where reading a novel where something like 90% of a book is describing the people, locations, objects and circumstances. The author of a novel would want to use exaggeration and more colourful words to underscore a uniquely important person, but "this week in trains" would probably de-emphasize the person and focus on the train-coupler.
The funny thing about this is that this also appears in bad human writing. We would be better off if vague statements like this were eliminated altogether, or replaced with less fantastical but verifiable statements. If this means that nothing of the article is left then we have killed two birds with one stone.
That's actually putting into words, what I couldn't, but felt similar. Spectacular quote
That sounds like Flanderization to me https://en.wikipedia.org/wiki/Flanderization
From my experience with LLMs that's a great observation.
I particularly like (what I assume is) the subtle paean to Ted Chiang's "Blurry Jpeg of the Web" in there.
<https://www.newyorker.com/tech/annals-of-technology/chatgpt-...>
Outstanding. Praise wikipedia, despite any shortcomings wow, isn't it such a breath of fresh air in the world of 2026.
I think that's a general guideline to identify "propaganda", regardless of the source. I've seen people in person write such statements with their own hands/fingers, and I know many people who speak like that (shockingly, most of them are in management).
Lots of those points seems to get into the same idea which seems like a good balance. It's the language itself that is problematic, not how the text itself came to be, so makes sense to 100% target what language the text is.
Hopefully those guidelines make all text on Wikipedia better, not just LLM produced ones, because they seem like generally good guidelines even outside the context of LLMs.