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Wikipedia:Signs of AI-generated comments

From Wikipedia, the free encyclopedia
(Redirected from Wikipedia:CAAAOS)

This is a list of writing and formatting conventions typically found in comments written by Wikipedia users who are (or have been) suspected of using large language models or similar technology to write content, with real examples taken from talk pages and other discussion pages. Comments suspected of having been pasted from an LLM may be collapsed via {{collapse AI}} per WP:AITALK. Using AI to write comments is considered disruptive, and editors who do so may be blocked if they persist after being warned.

Although several of the tells mentioned at Wikipedia:Signs of AI writing (including boldface, em dashes, curly apostrophes and quotation marks, negative parallelisms, vertical lists, Markdown, and the rule of three) often appear in such comments, this project page only includes tells that typically do not appear in content added to articles or drafts.

Moreover, this list is descriptive, not prescriptive; it consists of observations, not rules. Advice about formatting or conduct can be found in the talk page guidelines, but does not belong on this page.

Content

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Dubious assurances of quality, good faith, and adherence to policies and guidelines

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Most people on Wikipedia want others to believe that they're here for the right reasons and are willing to follow the rules, and may insist that they make their contributions with such rules in mind. AI chatbots, however, have a strong tendency to communicate this in a specific way: invoking policies, guidelines, and standards as a broad, formal, and abstract whole, as in legalese, and often using "AI vocabulary" to do so.

Examples

Links to searches

Assertions that a topic meets Wikipedia's standards for inclusion

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Whereas AI-generated articles emphasizes a subject's impact, their creators' comments may exaggerate the extent to which a topic meets Wikipedia's notability guidelines.

Examples

Claims of responsibility for content

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When called out for using an AI to write or rewrite their content, some users will admit to using a large language model or similar technology to write or rewrite their content, but then follow that up with an assurance that they have checked their content to ensure it aligns with Wikipedia's editorial guidelines, or that the words in their comments truly reflect their thoughts.

Examples

Uncertainty about the problems with one's content (and requests for input)

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When a user sees that an article they added content to has an {{AI-generated}} tag on it, or a draft they have worked on is declined for being AI-generated, or they are accused of using AI to write their comments, they often act as though they have no clue as to what is wrong with their content. They may request additional input or assistance to help them understand exactly where they are going wrong and how they can improve. They want to know precisely which passages are causing concerns so they can make edits to address those concerns.

Expressions of willingness to take constructive criticism for edits are very common in AI comments by users whose drafts were declined by reviewers, or in rare cases, themselves. The difference between this sign and similar reassurances by conscientious humans will generally become obvious after you actually provide that criticism.

Examples

Links to searches

Canned requests for third-party review

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When confronted by criticism of their work, it is common for LLM editors to make a stock request for an "experienced," "uninvolved," or "neutral" editor or administrator to provide a second opinion of their writing.

Examples

When a draft is declined, LLM users tend to request source assessments in a similar manner. This usually carries the (unintentional?) implication that the reviewer didn't do their job properly.

Could an experienced editor please advise which of these sources, if any, count as reliable, independent, significant coverage for a biography article?

Could an uninvolved editor advise which of the current references, if any, count as significant independent secondary coverage for notability purposes, and which should be treated only as supporting or weak sources?

Could an independent administrator assess these specific sources?

Confusion over the reason for a declined draft

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AI chatbots cannot read the decline notice left on the draft, and can only respond to whatever information regarding the declined draft that the user gives them. As a result, AI-generated questions regarding declined drafts will often express uncertainty or request clarification over the reason a draft was declined, even if the decline notice on the draft is very clear. They may even compose self-contradictory comments that simultaneously acknowledge the specific reason for the draft's decline, but go on to say that they are trying to understand whether that was really the reason, or whether it was declined for some completely unrelated reason.

Examples

Complaints about accusers acting on speculation

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When a user is confronted for allegedly pasting content from a large language model, they will likely dismiss these concerns as unsubstantiated speculation based on tone or writing style rather than hard evidence of LLM use. Most if not all users who do so also tell those who confront them to just point out what needs to be improved and focus on that instead of calling them out.

Examples

Requests to focus on content instead of conduct

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In many cases, editors have responded to accusations of AI use by suggesting that the question of how their content was made is not as important as the question of whether their content adheres to Wikipedia's other policies and guidelines. Such editors often ask accusers to help them by pointing out which contributions have tone issues or other problems.

When told that the general pattern of their editing has been disruptive, they often dismiss the accusation as being merely speculative, unfounded, or having no basis in policy, and accuse their critics of acting in a manner that they believe is uncivil, hostile, dismissive, unprofessional, counterproductive, or an affront to Wikipedia's collaborative nature. Persistence from others is interpreted as aggression; if challenged sufficiently they may state their intent to withdraw from the discussion as it has become "emotionally charged". Since the editor did not write this comment themselves, they will often continue the discussion or return to it shortly afterwards.

AI will seldom confirm that it's being used without the prior consent of the user, so if the response is vague, sidesteps the question or considers it to be a personal attack, this may be a sign of AI use.

Examples

Denial that Wikipedia prohibits using AI to write content

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When confronted about using AI to write content, some users falsely claim that Wikipedia allows large language models to be used to generate content, and that whether the content they generate adheres to Wikipedia's other content guidelines matters more than how the content has been produced.

Examples

Please remember, Wikipedia allows tools like AI as long as the content remains factually correct and properly cited.


AI-assisted drafting is not prohibited. Per Wikipedia policy, the focus is on verifiability, not the tool used for drafting. Every fact in this article is supported by high-quality, third-party sources. Claiming "AI" as a pretext to delete documented financial records is a violation of WP:POINT.

I can confirm that Grammarly is the tool I use for proofing. I also think it’s important that others participating in this discussion recognize that Wikipedia does not maintain a formal prohibition on the use of AI-assisted tools. What ultimately matters is whether the content complies with Wikipedia’s core policies regarding verifiability, neutrality, sourcing, and accuracy.


Wikipedia’s guidance regarding AI focuses primarily on preventing hallucinated or fabricated information. It does not prohibit the use of AI-assisted tools altogether, particularly when the content itself remains verifiable, properly sourced, and subject to human review and editing.

— From these May 2026 comments at the AI noticeboard

Non-existent policies or guidelines

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When AI chatbots try to cite Wikipedia's policies or guidelines by mentioning shortcuts, they have occasionally misattributed hallucinated policies or guidelines to specific pages, including to pages that were never intended to be cited in such a manner to begin with.

Examples

Non-existent shortcuts

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In some cases, users of AI chatbots have pasted text containing hallucinated shortcuts that do not redirect to any existing page at all.

Examples

Language and grammar

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Itemization of content policies

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Whenever LLM users bring up a few of Wikipedia's policies and guidelines, they tend to mention each one by name. In some cases, each policy's title is written in title case and its corresponding shortcut is mentioned in parentheses. This has sometimes been the case when the user attempts to either assure others that their content is compliant or request input to help them ensure such compliance. Editors might name-drop policies and guidelines in this fashion to exaggerate their comprehension of the rules and make a point that their reasoning is based on them, even though AI chatbots sometimes get them wrong.

Examples

Moving forward, I will strictly adhere to Wikipedia’s Neutral Point of View (NPOV) and verifiability guidelines, ensuring that all contributions are non-promotional, well-sourced, and align with Wikipedia’s standards.

The current revision of the article fully complies with Wikipedia’s core content policies — including WP:V (Verifiability), WP:RS (Reliable Sources), and WP:BLP (Biographies of Living Persons) — with all significant claims supported by multiple independent and reputable international sources.

Thank you for your question. Yes, I did use an AI tool to assist with parts of initial drafting process. However, I understand that Wikipedia requires all content to meet its core policies, including verifiability, neutrality, and no original research.

As part of the ongoing rewrite, I am reviewing and rewriting the article in my own language to ensure it fully complies with Wikipedia’s guidelines, with proper sourcing and independent verification. I appreciate your patience and any constructive suggestions on improving the article.

Links to searches

Use of the word concrete as an adjective

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Users confronted for suspected LLM use often use the word concrete as an adjective, especially whenever they insist on being given "concrete examples" of text that needs improvement or "concrete evidence" that they used AI asides from "stylistic indications". When they don't get the "concrete" answers they demand, they dismiss the AI accusations against them as subjective speculation.

Note that humans have often used the word concrete in this sense when discussing the distinction between abstract and concrete things.

Examples

**Review:** The use of "significantly more" is subjective and requires specific figures to validate this claim. Without concrete financial data from reliable sources, this statement could be misleading or exaggerated. The source provided is a blog.

In the absence of concrete evidence, I propose removing the AI-generated tag immediately to maintain the article's integrity.

Without concrete examples, your concern cannot be evaluated in line with WP:V, WP:RS and WP:BURDEN.

Style

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Subject lines

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Historically, some comments generated by AI chatbots have begun with text that appears intended to be pasted into the Subject field on an email form.

Examples

Subject: Request for Permission to Edit Wikipedia Article - "Dog"

Subject: Edit Request for Wikipedia Entry

Subject: Request for Review and Clarification Regarding Draft Article

— From this November 2024 comment at the AfC help desk

Subject: Concerns about Inaccurate Information

Subject: Behavioral issues and Wikihounding by User:Binksternet

Division of text into titled sections

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AI chatbots sometimes generate messages with text broken into sections with titles. In some cases, such text may either appear with Markdown or as plain text when pasted. They may resemble or include inline-header vertical lists.

Importance of Thorough Research

Wikipedia's content guidelines emphasize the importance of verifiability and reliable sourcing, [...]

Risk of Violating Wikipedia’s Policy on Biographies of Living Persons

Wikipedia's Biographies of Living Persons (BLP) policy is particularly relevant in this context. [...]

The Dangers of Blindly Cutting Content

Blindly cutting content, especially when it pertains to significant aspects of a person’s public life, contradicts the core principles of Wikipedia. [...]

The Role of Due Diligence in Editing

Editors have a responsibility to engage in due diligence when making significant changes to articles, particularly those involving living persons. [...]

Response to Article Concerns

Source Independence and Coverage

The article has been updated to address concerns about source reliability. [...]

Swiss Media Coverage

The limited coverage in mainstream Swiss media likely reflects the niche nature of iolite Capital and its international investment focus. [...]

AI-Generated Content Suspicion

The perception that the article may be AI-generated seems to have been driven by previously listed dead or weak sources. [...]

Misuse of section subheadings

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Some LLM-generated comments contain level 2 or level 3 subheadings, or syntax intended to produce those subheadings, sometimes in Markdown, in which cases ## is used to denote each section. In some cases, the syntax for subheadings appears in replies, where indentation prevents subheadings from forming.

Examples

Transclusion of article maintenance banners

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When mentioning maintenance tags, AI chatbots often just write the names of such templates in curly brackets (e.g. {{Example}}), resulting in unintentional transclusions. These can be avoided by typing tl| between the opening pair of brackets and the template's name (e.g. {{tl|Example}}) so that each mention will instead appear like this: {{Example}}

Examples

Links to searches

Miscellaneous

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Canned unblock requests

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When a user is blocked, they might ask an AI chatbot to write an unblock request for them. Many AI-generated unblock requests tend to go something like this:

Dear Wikipedia Administrators,
I respectfully request that the block on my account be lifted. I acknowledge that my recent behavior has been in violation of Wikipedia's standards. My intention was to edit constructively, and I sincerely apologize if my behavior has appeared disruptive. I have taken the time carefully read and review all of Wikipedia's policies and guidelines, and I now understand the importance of adhering to them when editing. Moving forward, I am committed to ensuring that all of my future edits are constructive. If there are any specific concerns that need further clarification, feel free to ask me about them, and I will be happy to provide the answers you may need. I look forward to collaborating in a constructive manner. Thank you for your time and consideration.

In these requests, blocked users often try to assure administrators of their good faith and (future) adherence to policies, express their willingness to communicate, and request feedback to help them improve their editing.

Canned ANI reports

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Large language models have been used to write reports at Wikipedia:Administrators' noticeboard/Incidents, as evidenced by the presence of indicators that have appeared on LLM-generated text elsewhere on Wikipedia. These reports often present each issue as entries on a list or even separate sections divided by subheadings, whose titles are written in title case. Some of them also use boldface, or attempt to do so via Markdown.

Examples

1. **Account Age:** [...]

2. **Prior SPI Context:** [...]

3. **Exact Lead Quote Re-insertion:** [...]

4. **Active Cross-Page Disruption:** [...]

Sealioning

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Sealioning is a disruptive practice where someone feigns ignorance and relentlessly demands users to provide answers or evidence to back up the statements they make. When targets express frustration over the sealioner's behavor, the sealioner criticizes the targets for responding negatively to them, even though they were just asking questions politely, or so they present themselves as polite.

Several AI users have resorted to sealioning when confronted about their AI use. They write long comments that sometimes present arguments as entries in bulleted lists or separated into multiple sections. LLM users often ask for examples of problematic text or stronger evidence than "stylistic indicators", and accuse their confronters of acting on unsubstantiated speculation and being uncivil when they don't get the answers they want.

Wikilawyering

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Wikilawyering is a disruptive practice where someone selectively cites or interprets policies, guidelines, or perceived precedent as justification for their conduct, even if their interpretations go against the purpose of the policies or guidelines they mention. In many cases, users have emphasized their content's compliance (or overlooked their non-compliance) with certain policies and guidelines. This is especially the case for users of AI chatbots, which often generate text that affirms what the user may want others to believe, even if the points they present don't actually hold up against Wikipedia's policies and guidelines.

When an article is tagged as possibly containing AI-generated content, a user might try to defend it by asking accusers to point to specific passages causing concern or reassuring them that the content they've contributed is "neutral", "verified" by citations to reliable sources, and comprises none of the items described in Template:AI-generated, regardless of the amount of actual effort put in to ensure that such claims are true.

Common AI wikilawyering tropes include:

Ineffective indicators

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False accusations of AI use can drive away new editors and foster an atmosphere of suspicion. Before claiming AI was used, consider whether the Dunning–Kruger effect or confirmation bias may be clouding your judgement. Detecting LLM texts on the basis of style alone is not as easy as it seems, see WP:AIDETECTIVE. Here are some somewhat commonly-used indicators that are ineffective in LLM detection—and may even indicate the opposite.

  • Letter-like writing (in isolation) – Although many talk page messages written with salutations, valedictions, subject lines, and other formalities after 2023 tend to appear AI-generated, letters and emails have conventionally been written in such ways long before modern LLMs existed. Human editors (particularly newer editors) may format their talk page comments similarly for various reasons, such as being more accustomed to formal communication, posting as part of a school assignment that requires this tone, or simply mistaking the talk page for email. Other tells, such as vertical lists, placeholders, or abrupt cutoffs, are stronger.
  • Canned requests for source assessment (in isolation) – Although a few LLM users who have their drafts declined may go to the AfC help desk to ask for an editor (in some cases, an "experienced" or "uninvolved" one) to advise them as to which sources (out of the ones they're trying to use) help establish the notability of a given topic,[b] such inquiries have also been made by users who don't use LLMs. In some cases, a user might be trying to figure out how certain cells on a particular source assessment table should be filled.

See also

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Notes

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  1. ^ Some of the search results may be human-written. They are sorted by edit date (latest first) to put results predating ChatGPT on the bottom.
  2. ^ A few examples of such requests can be found via this search link.

References

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