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I found the page Wikipedia:Signs of AI Writing[1] very interesting and informative. It goes into a lot more detail than the typical "em-dashes" heuristic. [1]: https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
I found the page Wikipedia:Signs of AI Writing[1] very interesting and informative. It goes into a lot more detail than the typical "em-dashes" heuristic. [1]: https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
This is very good, but I'm surprised the term "game-changer" is not mentioned there. From my observations this is used a lot in LLM texts.
I found the page Wikipedia:Signs of AI Writing[1] very interesting and informative. It goes into a lot more detail than the typical "em-dashes" heuristic. [1]: https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
Ironically this is a goldmine for AI labs and AI writer startups to do RL and fine-tuning.
I found the page Wikipedia:Signs of AI Writing[1] very interesting and informative. It goes into a lot more detail than the typical "em-dashes" heuristic. [1]: https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
There was a paper recently about using LLMs to find contradictions in Wikipedia, i.e. claims on the same page or between pages which appear to be mutually incompatible. https://arxiv.org/abs/2509.23233 I wonder if something more came out of that. Either way, I think that generation of article text is the least useful and interesting way to use AI on Wikipedia. It's much better to do things like this paper did.
That’s super interesting. I had a similar idea about 18 months ago. I think the biggest opportunity is building a knowledge graph based on Wikipedia and then checking against the graph when new edits are made . Detect any new assertions in the edit, check for conflicts against the graph, and bring up a warning along with a link to all the pages on Wikipedia that the new edit is contradicting. If the new edit is bad,…
Sounds pretty relevant
There was a paper recently about using LLMs to find contradictions in Wikipedia, i.e. claims on the same page or between pages which appear to be mutually incompatible. https://arxiv.org/abs/2509.23233 I wonder if something more came out of that. Either way, I think that generation of article text is the least useful and interesting way to use AI on Wikipedia. It's much better to do things like this paper did.
You can easily do this with normal GPT 5.2 in ChatGPT, just turn on thinking (better if extended) and web search, point a Wikipedia page to the model and tell it to check the claims for errors. I've tried it before and surprisingly it finds errors very often, sometimes small, sometimes medium. The less popular the page you linked is, the more likely it'll have errors. This works because GPT 5.x actually properly use…
I found the page Wikipedia:Signs of AI Writing[1] very interesting and informative. It goes into a lot more detail than the typical "em-dashes" heuristic. [1]: https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
Earlier quoted context omitted.
Ironically this is a goldmine for AI labs and AI writer startups to do RL and fine-tuning.
Seems more like the kind of thing you would make prompts using. I can totally see someone taking that page and throwing it into whatever bot and going "Make up a comprehensive style guide that does the opposite of whatever is mentioned here".
A major flaw of Wikipedia is that much of it is simply poorly written. Repetition and redundancy, ambiguity, illogical ordering of content, rambling sentences, opaque grammar. That should not be surprising. Writing clear prose is a skill that most people do not have, and Wikipedia articles are generally the fruit of collaboration without copy editors.
AI is perfectly suited to fixing this problem. I recently spent several hours rewriting a somewhat important article. I did not add or subtract information from the article, I simply made it clearer and more concise. I came away convinced that AI could have done as good a job - with supervision, of course - in a fraction of the time. AI-assisted copy-editing is not against Wikipedia rules. Yet as things stand, there are no built-in tools to facilitate it, doubtless because of the ambient suspicion of AI as a technology. We need to take a smarter approach.
Earlier quoted context omitted.
An 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."
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" w…