Undue emphasis on significance, legacy, and broader trends
LLM writing often puffs up the importance of the subject matter by adding statements about how arbitrary aspects of the topic represent or contribute to a broader topic.[3][4] There is a distinct and easily identifiable repertoire of ways that it writes these statements.[5]
Similarly, LLMs act as if the best way to prove that a subject is notable is to hit readers over the head with claims of notability, often by listing sources that a subject has been covered in. They may or may not provide additional context as to what those sources have actually said about the subject, and often inaccurately attribute their own superficial analyses to the source. This is more common in text from newer AI tools (2025 or later).
AI chatbots tend to insert superficial analysis of information, often in relation to its significance, recognition, or impact.[6] This is often done by attaching a present participle ("-ing") phrase at the end of sentences, sometimes with vague attributions to third parties (see below).[6][3]
For the purpose of Wikipedia, such comments are usually synthesis or unattributed opinions. Newer chatbots with retrieval-augmented generation (for example, an AI chatbot that can search the web) may attach these statements to named sources—e.g., "Roger Ebert highlighted the lasting influence"—regardless of whether those sources say anything close.
Promotional and advertisement-like language
LLMs have serious problems keeping a neutral tone. Even when prompted to use an encyclopedic tone, and even when editors have no promotional interest in a topic, their output will often tend toward advertisement-like writing, or like the prose of a travel guide. This may happen when generating new text or rewriting existing text; they often insert promotional language while claiming they removed it.
Note: Not all promotional or spammy writing is AI-generated. LLMs tend to over-use the same set of promotional phrases no matter what the topic. Also, older LLMs (e.g., GPT-4) tend to output more blatantly positive text than newer LLMs, which are more likely to be subtly positive.
Vague attributions and overgeneralization of opinions
AI chatbots tend to attribute opinions or claims to some vague authority—a practice called weasel wording.
Outline-like conclusions about challenges and future prospects
Many LLM-generated Wikipedia articles include a "Challenges" section, which typically begins with a sentence like "Despite its [positive/promotional words], [article subject] faces challenges..." and ends with either a vaguely positive assessment of the article subject,[1] or speculation about how ongoing or potential initiatives could benefit the subject. Such paragraphs usually appear at the end of articles with a rigid outline structure, which may also include a separate section for "Future Prospects."
Note: This sign is about the rigid formula, not simply the mention of challenges or challenging.