Ironically enough I just started an "Encylopedia of AI" project, and I've decided to generate all of the content with DeepSeek, with me being an editor/reviewer. I'm not really doing it for anyone except myself but I'm quite enjoying it so far. https://enkuklios.com/
Much of wikipedia is written by one person, Steven Pruitt. He has created more than 33,000 Wikipedia articles. People rightly credit him for cataloging obscure topics even though much of what he writes is rote.
But now when AI does it, it's deemed bad? If it's factual and at all useful to someone, why not leave it? I feel like we're waging the same battles when Wikipedia became an open encyclopedia. Surely you need to control who can edit right? We all know how that ended.
But he's edited a "third" of en.wikipedia according to his article which is genuinely astounding to me. Surely much of that has to have been automated?
There will be a point in the future, possibly near future, when AI will be capable of reliably producing encyclopedic content, and we'll have tools to verify that the content produced passes reasonable standards.
Today is not the day. Especially not regarding the verification.
Wikipedia is a tertiary source. Its editors are parroting secondary sources. (No disrespect intended.) There's no reason why LLMs couldn't be good at that.
I would rather be worried about the quality of all the primary and secondary sources going forward.
There is a reason why LLMs can not be good at this: They don't know what they are quoting or citing. A different kind of model with factoid provenance could do it?
What do you mean by this? I have passed very large documents into LLMs and they are able to accurately extract quotes and identify their location in the source documents. I don’t see any reason to think that they couldn’t accurately quote from and cite existing literature.
LLMs cook up new sentences based on probability, based on everything in their training and their context. There's just nothing in their model that traces provenance of facts, they physically don't store that information. Once they have written a sentence, they can't tell you from which work they got this information.
There are tools where they try to retroactively add references, the LLM interfaces try to do this, but it's usually inaccurate.
I think of the way a scientific article works. "The sky is blue (Pearson, 1992)." The writer is fully aware what the factual statement is and which publication supports it (not just that, but which kind of publication - primary, secondary source, peer reviewed?). An LLM can try to imitate this, but the actual information to support this does not exist in its training.
That's not how academic literature works. The structure is like this: Statement (Citation). The citation is not an affirmation of truth but a content declaration; this statement is supported by this publication. The receipts are there, so that you can trace the argumentation and make your own conclusions if necessary.
'Knowledge' formed this way is a living, evolving graph, and sometimes links are broken (findings are reevaluated or disproven); having the content declaration so clearly in the open makes it easier to deal with this evolving landscape.
They can. I had one doing original OCR of a scanned piece yesterday. It found relevant page numbers and understood the context. It read the whole book to find the relevant cites. Even went though the genealogy table and found ground breaking references no one noticed before. Total cost under 5 dollars and time was less than 20 mins.
That's really not how probabilistic systems work though. They're great at getting 80% of the way to the goal but the last 20% is always excruciatingly difficult. I really don't intend that a knock because LLMs are useful, but they also aren't magic boxes where anything is possible.
Even putting aside accuracy, this ignores the issue of data bias when training such models.
Even subtle bias can manifest in significant ways. Given that the predominant market leaders in producing such models are currently : A country that's attempting to squeeze the word "america" in any geographic formation possible + rewriting history to put itself first, and a country that has perfected censorship en-mass and already actively doctors its own history.
I'm not convinced giving LLMs carte blanche to write authoritative sources that are already prone to isn't just needlessly reckless and short-sighted.
Wikipedia already struggles with issues in idealogical bias from authors - human validation (somewhat)helps mitigate that. You could argue that human validation is still prone to same bias - but you can always draw on a more diverse pool of authors, with LLM validation you're pretty much at the mercy of a pretty homogeneous pool of data & a handful of self-serving corporates.
the thing is: if AI-Generated Content (if correct, obviously) is just some topics learnt from humans, what's the difference between something correct written by a machine and something correct written by a human?
Why to multiply instead of going to the same place for knowledge? It is almost like you want to make a different Wikipedia for conservatives and liberals because one cannot tolerate a tone or style of the other. Probably we should dedicate dozens of sites for the same topic depending on the writing styles of the humans too. There are a few out there.
In worst case we might have an 'AI language' (or languages) instead, so the sensitive souls could find a safe heaven from it elsewhere, choosing something else. Perhaps one along the idea of 'simple English', have it 'AI English'. Or something else satisfies those looking for a specific way of delivery they can handle exclusively.
I think knowledge is the important here, not the tone.
Honestly Wikipedia is just fine being written by humans. It's not the lack of copy that is holding Wikipedia back.
However,
> Perhaps one along the idea of 'simple English', have it 'AI English'.
That's actually an interesting idea. It could work, but I don't personally think it would be better than the English Wikipedia.
Also, are you expecting that humans would still be on the loop by deciding what gets added to AI English Wikipedia, or you think that LLMs should get to decide that too?
> It is almost like you want to make a different Wikipedia for conservatives and liberals because one cannot tolerate a tone or style of the other.
Many conservative worldview elements such as climate change denialism, creationism or anti LGBT view (e.g. LGBT being "unnatural") are not backed by reality because they run counter to scientifical consensus. And that's before we go into the outlandish bullshit conspiracy crap like "ZOG/zionist occupied government", "flat earth", "9/11 was faked", "the moon landing was faked" and the likes that gains more and more momentum among "conservative" people.
You cannot make a Wikipedia appealing to conservatives.
Not sure what question you're supposing he asked which was in the article. OP's point seemed to be that correct words written by a human could be effectively the same as correct words written by a machine, supposing the machine managed to be correct by learning from human content. The 'obvious' part is this wouldn't be true if the AI content was incorrect. At least that is what I got.
Almost all of these websites or platforms are doing these massive cleanup to remove ai content from them. Well appreciated.
Ironically enough I just started an "Encylopedia of AI" project, and I've decided to generate all of the content with DeepSeek, with me being an editor/reviewer. I'm not really doing it for anyone except myself but I'm quite enjoying it so far. https://enkuklios.com/
Much of wikipedia is written by one person, Steven Pruitt. He has created more than 33,000 Wikipedia articles. People rightly credit him for cataloging obscure topics even though much of what he writes is rote.
But now when AI does it, it's deemed bad? If it's factual and at all useful to someone, why not leave it? I feel like we're waging the same battles when Wikipedia became an open encyclopedia. Surely you need to control who can edit right? We all know how that ended.
https://en.wikipedia.org/wiki/Steven_Pruitt
Maybe when Steven Pruitt starts hallucinating facts we can make the "both sides" argument. But until then....
He just farts lies when he's not sharting subjectivity instead.
> Much of wikipedia is written by one person, Steven Pruitt. He has created more than 33,000 Wikipedia articles.
There are over 7m articles in English Wikipedia.
But he's edited a "third" of en.wikipedia according to his article which is genuinely astounding to me. Surely much of that has to have been automated?
Related:
"Low-background Steel: content without AI contamination (jgc.org)" 10.jun.2025 https://news.ycombinator.com/item?id=44239481 269 comments
"Wikipedia: Database Download (wikipedia.org)" 27.apr.2025 https://news.ycombinator.com/item?id=43811732 99 comments
"Low Background Steel (lowbackgroundsteel.ai)" 27.apr.2025 https://news.ycombinator.com/item?id=43814531 2 comments
There will be a point in the future, possibly near future, when AI will be capable of reliably producing encyclopedic content, and we'll have tools to verify that the content produced passes reasonable standards.
Today is not the day. Especially not regarding the verification.
A parrot can be never a true source of information.
Wikipedia is a tertiary source. Its editors are parroting secondary sources. (No disrespect intended.) There's no reason why LLMs couldn't be good at that.
I would rather be worried about the quality of all the primary and secondary sources going forward.
There is a reason why LLMs can not be good at this: They don't know what they are quoting or citing. A different kind of model with factoid provenance could do it?
>They don't know what they are quoting or citing.
What do you mean by this? I have passed very large documents into LLMs and they are able to accurately extract quotes and identify their location in the source documents. I don’t see any reason to think that they couldn’t accurately quote from and cite existing literature.
LLMs cook up new sentences based on probability, based on everything in their training and their context. There's just nothing in their model that traces provenance of facts, they physically don't store that information. Once they have written a sentence, they can't tell you from which work they got this information.
There are tools where they try to retroactively add references, the LLM interfaces try to do this, but it's usually inaccurate.
I think of the way a scientific article works. "The sky is blue (Pearson, 1992)." The writer is fully aware what the factual statement is and which publication supports it (not just that, but which kind of publication - primary, secondary source, peer reviewed?). An LLM can try to imitate this, but the actual information to support this does not exist in its training.
People reading a source can't know if it's true just by reading it either (though they could be fairly sure if it's false sometimes)
That's not how academic literature works. The structure is like this: Statement (Citation). The citation is not an affirmation of truth but a content declaration; this statement is supported by this publication. The receipts are there, so that you can trace the argumentation and make your own conclusions if necessary.
'Knowledge' formed this way is a living, evolving graph, and sometimes links are broken (findings are reevaluated or disproven); having the content declaration so clearly in the open makes it easier to deal with this evolving landscape.
They can. I had one doing original OCR of a scanned piece yesterday. It found relevant page numbers and understood the context. It read the whole book to find the relevant cites. Even went though the genealogy table and found ground breaking references no one noticed before. Total cost under 5 dollars and time was less than 20 mins.
The hallucination era is rapidly ending.
Then what you've said isn't true: it parrots an existing talking point, adding nothing.
That's really not how probabilistic systems work though. They're great at getting 80% of the way to the goal but the last 20% is always excruciatingly difficult. I really don't intend that a knock because LLMs are useful, but they also aren't magic boxes where anything is possible.
Grokipedia serves as an existence proof that it is possible.
https://en.wikipedia.org/wiki/Grokipedia#:~:text=that%20arti... It literally states itself on the frontpage that it's bad.
Funny though, as that admission of error makes it more reliable than if it tried to hide this fact.
Even putting aside accuracy, this ignores the issue of data bias when training such models.
Even subtle bias can manifest in significant ways. Given that the predominant market leaders in producing such models are currently : A country that's attempting to squeeze the word "america" in any geographic formation possible + rewriting history to put itself first, and a country that has perfected censorship en-mass and already actively doctors its own history.
I'm not convinced giving LLMs carte blanche to write authoritative sources that are already prone to isn't just needlessly reckless and short-sighted.
Wikipedia already struggles with issues in idealogical bias from authors - human validation (somewhat)helps mitigate that. You could argue that human validation is still prone to same bias - but you can always draw on a more diverse pool of authors, with LLM validation you're pretty much at the mercy of a pretty homogeneous pool of data & a handful of self-serving corporates.
the thing is: if AI-Generated Content (if correct, obviously) is just some topics learnt from humans, what's the difference between something correct written by a machine and something correct written by a human?
You can always read Grokipedia if this feels like a lost opportunity to you.
Why to multiply instead of going to the same place for knowledge? It is almost like you want to make a different Wikipedia for conservatives and liberals because one cannot tolerate a tone or style of the other. Probably we should dedicate dozens of sites for the same topic depending on the writing styles of the humans too. There are a few out there.
In worst case we might have an 'AI language' (or languages) instead, so the sensitive souls could find a safe heaven from it elsewhere, choosing something else. Perhaps one along the idea of 'simple English', have it 'AI English'. Or something else satisfies those looking for a specific way of delivery they can handle exclusively.
I think knowledge is the important here, not the tone.
> Why to multiply instead of going to the same place for knowledge?
Why write more than a book instead of just putting everything into one book?
This is a way of thinking from which it is hard to get out, I have no idea how to point it to you.
In case you were not aware, Grokipedia is not merely "conservative", but literally AI generated.
There is already a different Wikipedia for conservatives, https://www.conservapedia.com
Honestly Wikipedia is just fine being written by humans. It's not the lack of copy that is holding Wikipedia back.
However,
> Perhaps one along the idea of 'simple English', have it 'AI English'.
That's actually an interesting idea. It could work, but I don't personally think it would be better than the English Wikipedia.
Also, are you expecting that humans would still be on the loop by deciding what gets added to AI English Wikipedia, or you think that LLMs should get to decide that too?
> It is almost like you want to make a different Wikipedia for conservatives and liberals because one cannot tolerate a tone or style of the other.
Many conservative worldview elements such as climate change denialism, creationism or anti LGBT view (e.g. LGBT being "unnatural") are not backed by reality because they run counter to scientifical consensus. And that's before we go into the outlandish bullshit conspiracy crap like "ZOG/zionist occupied government", "flat earth", "9/11 was faked", "the moon landing was faked" and the likes that gains more and more momentum among "conservative" people.
You cannot make a Wikipedia appealing to conservatives.
Yes, why even put the slop on Wikipedia when you can get it directly from the source.
LLMs can't know things. They have no concept of veracity. In a place for knowledge they'd better be left out.
Not sure if you're being snarky or not, but the period at the end makes me think yes
What else would end the sentence?
A question mark? :)
"if correct, obviously" - please elaborate. I do not see how this is obvious. Or how being correct is obvious.
Not to mention this exact question is being discussed in the article.
What about:
Correct information written by AI vs incorrect, biased information written by humans?
As if AI output isn't biased?
Not sure what question you're supposing he asked which was in the article. OP's point seemed to be that correct words written by a human could be effectively the same as correct words written by a machine, supposing the machine managed to be correct by learning from human content. The 'obvious' part is this wouldn't be true if the AI content was incorrect. At least that is what I got.
If restaurant asks to only eat the food you bought there at their tables, the question is not "but what if food from other places also is good"?
Can't wikipedia ask who and it what way writes content on their pages?