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.
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 ignoring accuracy, this ignores the issue of data bias when training such models.
Wikipedia already struggles with issues in idealogical bias from authors - but thankfully human checks help (somewhat) to mitigate that.
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 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.
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/
Almost all of these websites or platforms are doing these massive cleanup to remove ai content from them. Well appreciated.
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.
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 ignoring accuracy, this ignores the issue of data bias when training such models.
Wikipedia already struggles with issues in idealogical bias from authors - but thankfully human checks help (somewhat) to mitigate that.
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.
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.
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.
Then what you've said isn't true: it parrots an existing talking point, adding nothing.