Have you ever read a passage of text and felt that "this doesn't sound like it was written by a human"? Your intuition might be right—but identifying AI-generated content cannot rely on guessing so-called "AI high-frequency words" like "delve" or "underscore." Recently, the Wikipedia editing team publicly released its internal "AI Writing Identification Guide," revealing for the first time the "behavioral fingerprints" of large language models (LLMs) in writing, providing the public with an operational and evidence-based method to identify AI-generated texts.

Since launching the "AI Cleanup Project" (Project AI Cleanup) in 2023, Wikipedia editors have faced millions of daily edits, accumulating a vast amount of AI writing samples. They found that automated detection tools are largely ineffective, and reliable judgment comes from a deep observation of language habits and narrative logic.

Five Common "Flaws" in AI Writing, Easily Recognizable

Vacuous Emphasis on Importance

AI often uses vague terms to highlight the value of a topic, such as "This is a critical moment" or "It reflects widespread impact," but lacks specific factual support—this "anxiety about importance" is extremely rare in encyclopedic entries written by humans.

Excessive Accumulation of Low-Value Media Reports

To prove that a person or event "deserves inclusion," AI frequently lists numerous marginal media exposures (such as a blog interview or a local radio segment), imitating the style of a resume, rather than citing authoritative, independent sources.

Abusive Use of "Present Participle" Summaries

Frequently using vague concluding phrases like "emphasizing the importance of..." or "reflecting the ongoing relevance of..." (grammatically known as "present participle phrases") creates the illusion of "deep analysis," but the content is actually hollow. Wikipedia editors say: "Once you notice this pattern, you'll find it everywhere."

Overuse of Ad-Like Adjectives

AI tends to use marketing jargon such as "picturesque," "breathtaking," or "clean and modern," making the text "sound like a TV commercial script," lacking the objective and restrained tone typical of encyclopedic writing.

Overly Structured but Lacking Insight

Paragraphs may appear logically clear and progressively structured, but they actually repeat the same expressions, lacking the critical thinking or unique perspective of a human author.

Why Are These Characteristics Hard to Eliminate?

The Wikipedia team pointed out that these "language fingerprints" are deeply rooted in AI's training logic: the model learns "how to write like a human" through massive internet texts, while the internet is full of self-promotion, SEO optimization, and content farm-style texts. Therefore, AI naturally inherits these "writing disorders of the digital age"—even if technology evolves, as long as the training data remains unchanged, these habits will be difficult to completely eliminate.

Public Awareness May Reshape the AI Content Ecosystem

The release of this guide marks a shift in AI content identification from "black box detection" to public participation in literacy education. When more and more readers can recognize AI patterns through common sense, those relying on AI to mass-produce content, fake news sites, or even academic misconduct will face greater risks.

AIbase believes that Wikipedia's move is not only a model of community self-governance, but also a warning to the entire generative AI ecosystem: true intelligence is not about fluent repetition, but about authenticity, restraint, and depth of thought. When the "language mask" of AI is removed, the unique value of human writing becomes even more prominent.