March 26, 2026 Wikipedia has banned the use of large language models to generate or rewrite article content on its English-language site. The move formalises how the platform will handle AI tools, limiting their role to tightly controlled editing and translation tasks.
The policy follows extended internal debate about how to manage LLMs at scale. Wikipedia administrator Chaotic Enby said earlier proposals failed because contributors agreed on the need for change but not on implementation, citing concerns that prior approaches were either too vague or too rigid.
Under the new rules, editors cannot use AI tools to create or substantially rewrite encyclopedia entries. The restriction targets the core of Wikipedia’s value: verifiable, human-reviewed content grounded in reliable sources.
There are two defined exceptions. Editors may use LLMs to refine their own writing, similar to a grammar or style tool, but must verify that the meaning remains accurate and supported by cited sources. The policy explicitly notes that “LLMs can go beyond what you ask of them and change the meaning of the text such that it is not supported by the sources cited.”
The second exception covers translation. AI tools can be used for an initial translation pass, but contributors must be fluent enough in both languages to validate accuracy and correct errors. As with writing assistance, responsibility for factual integrity remains with the human editor.
The policy applies only to the English Wikipedia, which operates independently from other language editions. Governance varies across regions: Spanish Wikipedia, for example, already prohibits using LLMs to create or expand articles, but does not include the same structured allowances for editing or translation support.
Enforcement remains a practical challenge. Detecting AI-generated text is still unreliable, and the policy acknowledges that some machine-generated content may slip through, particularly on less actively moderated pages. It also notes that human writing styles can resemble AI output, complicating identification efforts.
