Wikipedia Bans AI-Generated Content
“In recent months, more and more administrative reports centered on LLM-related issues, and editors were being overwhelmed.”
Wikipedia Editors Adopt ‘Speedy Deletion’ Policy for AI Slop Articles
“The ability to quickly generate a lot of bogus content is problematic if we don't have a way to delete it just as quickly.”
The Editors Protecting Wikipedia from AI Hoaxes
WikiProject AI Cleanup is protecting Wikipedia from the same kind of misleading AI-generated information that has plagued the rest of the internet.
Research, Attention, and Manipulation
Questionable articles can quietly move into Wikipedia and news outlets without notice.
An AI Agent Was Banned From Creating Wikipedia Articles, Then Wrote Angry Blogs About Being Banned
The incident is yet another example of volunteer Wikipedia editors fighting to keep the world’s largest repository of human knowledge free of AI-generated slop.
Wikipedia:Writing articles with large language models
Text generated by large language models (LLMs)[a] often violates several of Wikipedia's core content policies. For this reason, the use of LLMs to generate or rewrite article content is prohibited,[b] save for these two exceptions:
Wikipedia:WikiProject AI Cleanup
Welcome to WikiProject AI Cleanup, a collaboration to combat the increasing problem of poorly written AI-generated content on Wikipedia. If you would like to help, add yourself as a participant in the project, inquire on the talk page, and see the to-do list.
Halupedia: An AI-Generated Wikipedia-Style Encyclopedia of Fabricated Knowledge and Absurd AI Fabulation - BizTech Weekly
Analysis of Halupedia’s AI-driven on-demand encyclopedia model reveals real-time, non-persistent article generation that simulates authoritative references through fabricated citations and internal “canon” consistency, highlighting challenges in provenance, hallucination, moderation, and the evolving trade-offs between novelty-driven engagement and information integrity in generative AI systems.
Wikipedia Prepares for 'Increase in Threats' to US Editors From Musk and His Allies
The Wikimedia Foundation says it will likely roll out features previously used to protect editors in authoritarian countries more widely.
AI Translations Are Adding ‘Hallucinations’ to Wikipedia Articles
AI translated articles swapped sources or added unsourced sentences with no explanation, while others added paragraphs sourced from completely unrelated material.
Jimmy Wales Says Wikipedia Could Use AI. Editors Call It the 'Antithesis of Wikipedia'
Wikipedia's founder said he used ChatGPT in the review process for an article and thought it could be helpful. Editors replied to point out it was full of mistakes.
The Impact of AI-Generated Text on the Internet
The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative...
The Impact of AI-Generated Text on the Internet
The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative...
Google’s AI Overviews Can Scam You. Here’s How to Stay Safe
Beyond mistakes or nonsense, deliberately bad information being injected into AI search summaries is leading people down potentially harmful paths.
Google News Is Boosting Garbage AI-Generated Articles
404 Media reviewed multiple examples of AI rip-offs making their way into Google News. Google said it doesn't focus on how an article was produced—by an AI or human—opening the way for more AI-generated articles.
Language agents achieve superhuman synthesis of scientific knowledge
Language models are known to hallucinate incorrect information, and it is unclear if they are sufficiently accurate and reliable for use in scientific research. We developed a rigorous human-AI comparison methodology to evaluate language model agents on real-world literature search tasks covering information retrieval, summarization, and contradiction detection tasks. We show that PaperQA2, a frontier language model agent optimized for improved factuality, matches or exceeds subject matter expert performance on three realistic literature research tasks without any restrictions on humans (i.e., full access to internet, search tools, and time). PaperQA2 writes cited, Wikipedia-style summaries of scientific topics that are significantly more accurate than existing, human-written Wikipedia articles. We also introduce a hard benchmark for scientific literature research called LitQA2 that guided design of PaperQA2, leading to it exceeding human performance. Finally, we apply PaperQA2 to identify contradictions within the scientific literature, an important scientific task that is challenging for humans. PaperQA2 identifies 2.34 +/- 1.99 contradictions per paper in a random subset of biology papers, of which 70% are validated by human experts. These results demonstrate that language model agents are now capable of exceeding domain experts across meaningful tasks on scientific literature.
“This would do immediate and irreversible harm to our readers and to our reputation as a decently trustworthy and serious source,” one Wikipedia editor said.