# Signs of AI Writing

[![A screenshot of ChatGPT reading: "[header] Legacy & Interpretation [body] The "Black Hole Edition" is not just a meme — it's a celebration of grassroots car culture, where ideas are limitless and fun is more important than spec sheets. Whether powered by a rotary engine, a V8 swap, or an imagined fighter jet turbine, the Miata remains the canvas for car enthusiasts worldwide."](https://upload.wikimedia.org/wikipedia/commons/thumb/5/59/ChatGPT_response_screenshot_1.jpg/250px-ChatGPT_response_screenshot_1.jpg)](https://en.wikipedia.org/wiki/File:ChatGPT_response_screenshot_1.jpg)

_LLMs tend to have an identifiable writing style._

This is a list of writing and formatting conventions typical of [AI chatbots](https://en.wikipedia.org/wiki/AI_chatbot)
such as [ChatGPT](https://en.wikipedia.org/wiki/ChatGPT), with real examples taken from Wikipedia articles and drafts.
It is a [field guide](https://en.wikipedia.org/wiki/Field_guide) to help detect
[undisclosed AI-generated content](https://en.wikipedia.org/wiki/Wikipedia:LLMDISCLOSE) on Wikipedia. This list is
_descriptive_, not _prescriptive_; it consists of observations, not rules. Advice about formatting or language to avoid
in Wikipedia articles can be found in the [policies and guidelines](https://en.wikipedia.org/wiki/Wikipedia:PAG) and the
[Manual of Style](https://en.wikipedia.org/wiki/Wikipedia:MOS), but does not belong on this page.

This list is _not_ a ban on certain words, phrases, or punctuation. Not all text featuring these indicators is
AI-generated, as the [large language models](https://en.wikipedia.org/wiki/Large_language_model) that power AI chatbots
are trained on human writing, including the writing of Wikipedia editors. This is simply a catalog of very common
patterns observed over many thousands of instances of AI-generated text, _specific to Wikipedia._ While some of its
advice may be broadly applicable, some signs—particularly those involving punctuation and formatting—may not apply in a
non-Wikipedia context.

The patterns here are also only potential _signs_ of a problem, not _the problem itself_. While many of these issues are
immediately obvious and easy to fix—e.g., excessive boldface, poor use of language and punctuation, broken markup,
citation style quirks—they can point to less outwardly visible problems that carry
[much more serious policy risks](https://en.wikipedia.org/wiki/Wikipedia:AIFAIL). If LLM-generated text is polished
enough (initially or subsequently), those surface defects might not be present, but deeper problems can be. Please do
not merely treat these signs as the problems to be fixed; that could just make detection harder. The actual problems are
those deeper concerns, so make sure to address them, either yourself or by flagging them, per the advice at
[Wikipedia:Large language models §Handling suspected LLM-generated content](https://en.wikipedia.org/wiki/Wikipedia:Large_language_models#Handling_suspected_LLM-generated_content)
and [Wikipedia:WikiProject AI Cleanup/Guide](https://en.wikipedia.org/wiki/Wikipedia:WikiProject_AI_Cleanup/Guide).

The [speedy deletion policy](https://en.wikipedia.org/wiki/Wikipedia:Speedy_deletion) criterion
[G15](https://en.wikipedia.org/wiki/Wikipedia:G15) (LLM-generated pages without human review) is limited to the most
objective and least contestable indications that the page's content was generated by an LLM. There are three such
indicators, the first of which can be found in
[§Communication intended for the user](#communication-intended-for-the-user) and the other two in
[§Citations](#citations).

Do not solely rely on
[artificial intelligence content detection](https://en.wikipedia.org/wiki/Artificial_intelligence_content_detection)
tools (such as [GPTZero](https://en.wikipedia.org/wiki/GPTZero)) to evaluate whether text is LLM-generated. While they
perform better than might be achieved by chance, these tools have non-trivial error rates and cannot replace human
judgment.[^1] Detectors can be brittle to multiple factors such as text modifications (e.g. paraphrasing and spacing
changes) and the use of generative models not seen during detector training.[^2] By the same token, do not trust too
much in your own interpretation. Research shows that people who use LLMs heavily themselves can correctly determine
whether an article was generated by AI about 90% of the time, which means that if you are an expert user of LLMs and you
tag 10 pages as being AI-generated, you've probably falsely accused one editor.[^3] People who don't personally use LLMs
much do only slightly better than random chance (in both directions) for identifying AI-generated articles.[^3]

---

## Regression to the Mean

LLMs (and [artificial neural networks](https://en.wikipedia.org/wiki/Artificial_neural_network) in general) use
statistical algorithms to guess (infer) what should come next based on a large corpus of training material. It thus
tends to [regress to the mean](https://en.wikipedia.org/wiki/Regression_to_the_mean); that is, the result tends toward
the most statistically likely result that applies to the widest variety of cases. It can simultaneously be a strength
and a "tell" for detecting AI-generated content.

For example, LLMs are usually trained on data from the internet in which famous people are generally described with
positive, important-sounding language. Consequently, the LLM tends to omit specific, unusual, nuanced facts (which are
statistically rare) and replace them with more generic, positive descriptions (which are statistically common). Thus the
highly specific "inventor of the first train-coupling device" might become "a revolutionary titan of industry." It is
like shouting louder and louder that a portrait shows a uniquely important person, while the portrait itself is fading
from a sharp photograph into a blurry, generic sketch. The subject becomes simultaneously less specific and more
exaggerated.[^4]

This statistical regression to the mean, a smoothing over of specific facts into generic statements, that could equally
apply to many topics, makes AI-generated content easier to detect.

### Undue emphasis on symbolism, legacy, and importance

**Words to watch:** _stands/serves as_, _is a testament/reminder_, _plays a vital/significant/crucial/pivotal role_,
_underscores/highlights its importance/significance_, _reflects broader_, _symbolizing its ongoing/enduring/lasting
impact_, _key turning point_, _indelible mark_, _deeply rooted_, _profound heritage_, _steadfast dedication_...

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.[^5] There is a distinct and easily identifiable repertoire of ways
that it writes these statements.[^6]

> The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution
> of regional statistics in Spain. [...]
>
> The founding of Idescat represented a significant shift toward regional statistical independence, enabling
> [Catalonia](https://en.wikipedia.org/wiki/Catalonia) to develop a statistical system tailored to its unique
> socio-economic context. This initiative was part of a broader movement across Spain to decentralize administrative
> functions and enhance regional governance.
>
> Kumba has long been an important center for trade and agriculture. [...] The establishment of road networks connecting
> Kumba to other parts of the Southwest Region, such as Mamfe and Buea, helped solidify its role as a regional hub.

LLMs may include these statements for even the most mundane of subjects like etymology or population data. Sometimes,
they add hedging preambles acknowledging that the subject is relatively unimportant or low-profile, before talking about
its importance anyway.

### Examples

> During the [Spanish colonial period](<https://en.wikipedia.org/wiki/Spanish_Colonial_Period_(Philippines)>), the name
> _Bakunutan_ was hispanized to _Bacnotan_, a modification reflected in official documents preserved in the
> [National Archives](https://en.wikipedia.org/wiki/National_Archives_of_the_Philippines) in Manila. This etymology
> highlights the enduring legacy of the community's resistance and the transformative power of unity in shaping its
> identity.
>
> Though it saw only limited application, it contributes to the broader history of early aviation engineering and
> reflects the influence of French rotary designs on German manufacturers.

When talking about biology (e.g., when asked to discuss an animal or plant species), LLMs tend to over-emphasize
connections to the broader ecosystem or environment, even when those connections are tenuous or generic. LLMs also tend
to belabor the species' conservation status and research and preservation efforts, even if the status is unknown and no
serious efforts exist.

### Examples

> It plays a role in the ecosystem and contributes to Hawaii's rich cultural heritage. [...] Preserving this endemic
> species is vital not only for ecological diversity but also for sustaining the cultural traditions connected to
> Hawaii's native flora.
>
> Currently, there is no specific conservation assessment for _Lethrinops lethrinus_ by the International Union for
> Conservation of Nature (IUCN). However, the general health of the Lake Malawi ecosystem is crucial for the survival of
> this and other endemic species. Factors such as overfishing, pollution, and habitat destruction could potentially
> impact their populations.

### Undue emphasis on notability, attribution, and media coverage

**Words to watch:** _independent coverage_, _local/regional/national/[country name] media outlets_, _music/business/tech
outlets_, _active social media presence_

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](#superficial-analyses) to the source. This is more common in text from newer AI tools (2025 or
later).

Human-written press releases have of course also cited news clippings for decades, but LLMs specifically asked to write
a Wikipedia article often echo the exact wording of [Wikipedia's guidelines](https://en.wikipedia.org/wiki/Wikipedia:N),
such as "independent coverage."

### Examples

> She spoke about AI on CNN, and was featured in Vogue, Wired, Toronto Star, and other media. [...] Her insights have
> also been featured in _Wired_, _Refinery29_, and other prominent media outlets.
>
> Her views have been cited in _The New York Times_, _BBC_, _Financial Times_, and _The Hindu_.
>
> Its significance is documented in archived school event programs and regional press coverage, including the _Mesabi
> Daily News_, which regularly reviewed performances held there.

On Wikipedia specifically, LLMs often painstakingly emphasize their sources in the body text—even for trivial coverage,
uncontroversial facts, or other situations where a human Wikipedia editor would be more likely to either provide an
inline citation or no source at all.

### Examples

> The restaurant has also been mentioned in [ABC News](<https://en.wikipedia.org/wiki/ABC_News_(Australia)>) coverage
> relating to incidents in the surrounding precinct, underscoring its role as a well-known late-night venue in the city
> [of [Adelaide](https://en.wikipedia.org/wiki/Adelaide)].
>
> In the United States, university-based incubators and accelerators have expanded alongside these centers; an official
> Library of Congress review found that 31.5% of SBA
> [[Small Business Administration](https://en.wikipedia.org/wiki/Small_Business_Administration)] Growth Accelerator Fund
> Competition winners from 2014–2016 were university-based programs.

In articles about people/entities who use social media, LLMs will often note that they "maintain an active social media
presence" or something similar. This wording is particularly idiosyncratic to AI text and relatively uncommon on
Wikipedia before ~2024.

> The mall maintains a strong digital presence, particularly on Instagram, where it actively shares the latest updates
> and events. Forum Kochi has consistently demonstrated excellence in digital promotions, with high-quality, engaging,
> and impactful video content playing a key role in its outreach.

### Superficial analyses

**Words to watch:** _ensuring ..._, _highlighting ..._, _emphasizing ..._, _reflecting ..._, _underscoring ..._,
_showcasing ..._, _aligns with..._, _contributing to..._

AI chatbots tend to insert superficial analysis of information, often in relation to its significance, recognition, or
impact.[^7] This is often done by attaching a [present participle](https://en.wikipedia.org/wiki/Participle#Forms)
("-ing") phrase at the end of sentences, sometimes with [vague attributions](#vague-attributions-of-opinion) to third
parties (see below).[^7][^5]

While [many of these words are strong AI tells on their own](https://en.wikipedia.org/wiki/Wikipedia:AIWORDS),[^6][^8]
an even stronger tell is when the subjects of these verbs are facts, events, or other inanimate things. A person, for
example, can highlight or emphasize something, but a fact or event cannot. The "highlighting" or "underscoring" is not
something that is actually happening; it is a claim by a disembodied narrator about what something means.[^5]

Such comments are usually [synthesis](https://en.wikipedia.org/wiki/Wikipedia:SYNTH) and/or unattributed opinions in
wikivoice. Newer chatbots with
[retrieval-augmented generation](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) (for example, an AI
chatbot that can search the web) may attach these statements to
[named sources](#undue-emphasis-on-notability-attribution-and-media-coverage)—e.g., "Roger Ebert highlighted the lasting
influence"—regardless of whether those sources say anything close.

### Examples

> Douera enjoys close proximity to the capital city, Algiers, further enhancing its significance as a dynamic hub of
> activity and culture.
>
> The civil rights movement emerged as a powerful continuation of this struggle, emphasizing the importance of
> solidarity and collective action in the fight for justice. This historical legacy has influenced contemporary
> African-American families, shaping their values, community structures, and approaches to political engagement.
> Economically, the enduring impacts of systemic inequality have led to both challenges and innovations within
> African-American communities, driving a commitment to empowerment and social change that echoes through generations.
>
> Its bilingual monument sign, with inscriptions in both English and Spanish, underscores its role in bringing together
> Latter-day Saints from the United States and Mexico.
>
> These citations, spanning more than six decades and appearing in recognized academic publications, illustrate Blois'
> lasting influence in computational linguistics, grammar, and neology.
>
> It holds a pivotal place in the [East Central Railway Zone](https://en.wikipedia.org/wiki/East_Central_Railway_Zone)
> of [Indian Railways](https://en.wikipedia.org/wiki/Indian_Railways), serving as a major railway hub with historical
> significance. The station has [1,676 mm](https://en.wikipedia.org/wiki/5_ft_6_in_gauge_railway) (5 ft 6 in)
> [broad gauge](https://en.wikipedia.org/wiki/Broad_gauge) along with 8 tracks and 6 platforms. [...] Historically, it
> has been crucial for linking [Darbhanga](https://en.wikipedia.org/wiki/Darbhanga) with significant cities like
> [Delhi](https://en.wikipedia.org/wiki/Delhi), [Patna](https://en.wikipedia.org/wiki/Patna), and
> [Kolkata](https://en.wikipedia.org/wiki/Kolkata), facilitating the movement of passengers and goods. The station has
> supported various services, including passenger trains and express trains like the
> [Satyagrah Express](https://en.wikipedia.org/wiki/Satyagrah_Express) and
> [Mithila Express](https://en.wikipedia.org/wiki/Mithila_Express), contributing to the socio-economic development of
> the region. [...] Over the years, Darbhanga Junction has seen several upgrades and modernization efforts aimed at
> improving facilities and operational efficiency, reflecting its continued relevance in the regional and national
> transportation landscape.

### Promotional and advertisement-like language

**Words to watch:** _continues to captivate_, _groundbreaking_ (in the figurative sense), _stunning natural beauty_,
_enduring/lasting legacy_, _nestled_, _in the heart of_, _boasts a_...

LLMs have serious problems keeping a neutral tone, especially when writing about something that could be considered
"cultural heritage"—in which case they
[constantly remind the reader of its importance](#undue-emphasis-on-symbolism-legacy-and-importance).

### Examples

> Nestled within the breathtaking region of Gonder in Ethiopia, Alamata Raya Kobo stands as a vibrant town with a rich
> cultural heritage and a significant place within the Amhara region. From its scenic landscapes to its historical
> landmarks, Alamata Raya Kobo offers visitors a fascinating glimpse into the diverse tapestry of Ethiopia. In this
> article, we will explore the unique characteristics that make Alamata Raya Kobo a town worth visiting and shed light
> on its significance within the Amhara region.
>
> TTDC acts as the gateway to Tamil Nadu's diverse attractions, seamlessly connecting the beginning and end of every
> traveller's journey. It offers dependable, value-driven experiences that showcase the state's rich history, spiritual
> heritage, and natural beauty.

In a similar way, LLM chatbots also add promotional/positive-sounding language to text about companies, business, and
products, such that it sounds more like the transcript of a TV commercial.

> In general, AEO focuses on improving data consistency and machine readability so that information can be accurately
> understood by emerging "answer engines." Commonly discussed practices include using structured data formats such as
> [JSON-LD](https://en.wikipedia.org/wiki/JSON-LD) and [schema.org](https://en.wikipedia.org/wiki/Schema.org),
> maintaining the freshness and accuracy of published information, and aligning digital entities with open web standards
> like [Wikidata](https://en.wikipedia.org/wiki/Wikidata) and Schema.org.
>
> The SOLLEI's exterior design communicates a powerful emotional presence, staying true to Cadillac's signature bold
> proportions. Its low, elongated silhouette is highlighted by a wide stance and an extended coupe door, which enhances
> accessibility to the spacious rear cabin. Smooth, uninterrupted surfaces and a pronounced A-line accentuate the
> vehicle's overall length, while a sleek, low tail imparts a sense of refined dynamism. A mid-body line runs seamlessly
> from the headlamps to the taillights, reinforcing the car's cohesive and elegant design. Traditional door handles have
> been replaced with discrete buttons, preserving the vehicle's clean and modern profile. In a nod to Cadillac's legacy
> of bold color choices, the exterior is finished in "Manila Cream"—a distinctive hue originally offered in 1957
> and 1958. This heritage color has been thoughtfully revived and hand-painted by Cadillac artisans, showcasing the
> brand's dedication to craftsmanship and historical reverence.

### Didactic, editorializing disclaimers

**Words to watch:** _it's important/critical/crucial to note/remember/consider_, _may vary_...

LLMs often tell the reader about things "it's important to remember." This frequently takes the form of "disclaimers" to
an imagined reader regarding safety or controversial topics, or disambiguating topics that vary in different
locales/jurisdictions.

### Examples

> The emergence of these informal groups reflects a growing recognition of the interconnected nature of urban issues and
> the potential for ANCs to play a role in shaping citywide policies. However, it's important to note that these
> caucuses operate outside the formal ANC structure and their influence on policy decisions may vary.
>
> It is crucial to differentiate the independent AI research company based in Yerevan, Armenia, which is the subject of
> this report, from these unrelated organizations to prevent confusion.
>
> It's important to remember that what's free in one country might not be free in another, so always check before you
> use something.

### Summaries and conclusions

**Words to watch:** _In summary_, _In conclusion_, _Overall_...

When generating longer outputs (such as when told to "write an article"), LLMs often add a section titled "Conclusion"
or similar, and will often end a paragraph or section by summarizing and restating its core idea.[^9]

### Examples

> In summary, the educational and training trajectory for nurse scientists typically involves a progression from a
> master's degree in nursing to a Doctor of Philosophy in Nursing, followed by postdoctoral training in nursing
> research. This structured pathway ensures that nurse scientists acquire the necessary knowledge and skills to engage
> in rigorous research and contribute meaningfully to the advancement of nursing science.

### Outline-like conclusions about challenges and future prospects

**Words to watch:** _Despite its... faces several challenges..._, _Despite these challenges_, _Challenges and Legacy_,
_Future Outlook_...

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.

### Examples

> Despite its industrial and residential prosperity, Korattur faces challenges typical of urban areas, including[...]
> With its strategic location and ongoing initiatives, Korattur continues to thrive as an integral part of the Ambattur
> industrial zone, embodying the synergy between industry and residential living.
>
> Despite its success, the Panama Canal faces challenges, including[...] Future investments in technology, such as
> automated navigation systems, and potential further expansions could enhance the canal's efficiency and maintain its
> relevance in global trade.
>
> Despite their promising applications, pyroelectric materials face several challenges that must be addressed for
> broader adoption. One key limitation is[...] Despite these challenges, the versatility of pyroelectric materials
> positions them as critical components for sustainable energy solutions and next-generation sensor technologies.
>
> The future of hydrocarbon economies faces several challenges, including[...] This section would speculate on potential
> developments and the changing landscape of global energy.
>
> Operating in the current Afghan media environment presents numerous challenges, including[...] Despite these
> challenges, Amu TV has managed to continue to provide a vital service to the Afghan population​​.
>
> For example, while the methodology supports transdisciplinary collaboration in principle, applying it effectively in
> large, heterogeneous teams can be challenging. [...] SCE continues to evolve in response to these challenges.

### Leads treating Wikipedia lists or broad article titles as proper nouns

In AI-generated articles about topics with a title that is not a
[proper name](https://en.wikipedia.org/wiki/Proper_name), such as a
[list](https://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Lists), the first sentence of the lead may introduce
and/or define the article's title as if it were a standalone real-world entity. While the
[MOS](https://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Lead_section#Format_of_the_first_sentence) does allow such
titles to be included at the beginning of the lead "in a natural way"; these AI leads tend not to be so natural.

### Examples

> "The Effects of Foreign language anxiety on Learning" refers to the feelings of tension, nervousness, and apprehension
> experienced when learning or using a language other than one's native tongue.
>
> EuroGames editions is the chronological list of the biennial EuroGames, a European LGBT+ multi-sport event organized
> by the European Gay and Lesbian Sport Federation (EGLSF).
>
> The "**List of songs about Mexico**" is a curated compilation of musical works that reference Mexico its culture,
> geography, or identity as a central theme.

---

## Language and Grammar

### Overused "AI vocabulary" words

**Words to watch:** _align/aligns/aligning with_,[^6][^8] _crucial_,[^1] _delve/delves/delving_ (pre-2025),[^6][^8][^1]
_emphasizing_,[^6][^8] _enduring_,[^8] _enhance/enhances/enhancing_,[^8][^1] _fostering_,[^8][^1]
_garnered/garnering_,[^6][^8] _highlight/highlighted/highlighting/highlights_ (as a verb),[^1] _interplay_,[^8]
_intricate/intricacies_,[^6][^8][^7] _key_ (as an adjective), _landscape_,[^8] _leveraging_,[^1]
_multifaceted_,[^6][^8][^7] _notably_,[^8] _nuanced_,[^6][^8] _realm_,[^8] _robust_,[^1] _seamless/seamlessly_,[^1]
_shed light on_, _showcasing_,[^8] _streamline_,[^1] _tapestry_,[^8] _testament_,[^1][^8]
_underpin/underpins/underpinning_,[^8] _underscore/underscores/underscoring_,[^8] _vibrant_,[^8][^7] _vital_,[^1] ...

Many studies have demonstrated that LLMs overuse certain words – especially compared to pre-2022 text, which is almost
certain to be human-written.[^6] These "AI vocabulary" words are also ubiquitous in AI-generated encyclopedias, such as
[Grokipedia](https://en.wikipedia.org/wiki/Grokipedia), and in AI-generated Wikipedia text. They often co-occur in LLM
output: where there is one, there are likely others.[^10] An edit introducing one or two of these words may not be a big
deal, but an edit (post-2022) introducing lots of them, lots of times, is one of the strongest tells for AI use.

The distribution of "AI vocabulary" is slightly different depending on which chatbot or LLM was used,[^7] and has
changed over time. For instance, the word _delve_ was famously overused by ChatGPT until 2025, when its incidence
dropped off sharply.[^11]

Please keep context in mind. For example, while the word "underscore" is overused in AI text, it can also refer to a
literal underline mark, or to [incidental music](https://en.wikipedia.org/wiki/Incidental_music).

### Examples

> Somali cuisine is an intricate and diverse fusion of a multitude of culinary influences, drawing from the rich
> tapestry of [Arab](https://en.wikipedia.org/wiki/Arab_cuisine),
> [Indian](https://en.wikipedia.org/wiki/Indian_cuisine), and [Italian](https://en.wikipedia.org/wiki/Italian_cuisine)
> flavours. This culinary tapestry is a direct result of Somalia's longstanding heritage of vibrant trade and bustling
> commerce. [...]
>
> Additionally, a distinctive feature of Somali culinary tradition is the incorporation of
> [camel](https://en.wikipedia.org/wiki/Camel) [meat](https://en.wikipedia.org/wiki/Meat) and
> [milk](https://en.wikipedia.org/wiki/Milk). They are considered a delicacy and serve as cherished and fundamental
> elements in the rich tapestry of Somali cuisine. [...]
>
> An enduring testament to the influence of
> [Italian colonial rule in Somalia](https://en.wikipedia.org/wiki/Italian_Somaliland) is the widespread adoption of
> [pasta](https://en.wikipedia.org/wiki/Pasta) and [lasagne](https://en.wikipedia.org/wiki/Lasagna) in the local
> culinary landscape, espicially in the south, showcasing how these dishes have integrated into the traditional diet
> alongside rice. [...]
>
> Additionally, Somali merchants played a pivotal role in the global coffee trade, being one of the first to export
> coffee beans.

### Negative parallelisms

Parallel constructions involving "not", "but", or "however" such as "Not only ... but ..." or "It is not just about ...,
it's ..." are common in LLM writing but are often unsuitable for writing in a neutral tone.[^1][^11]

### Examples

> **Self-Portrait** by Yayoi Kusama, executed in 2010 and currently preserved in the famous Uffizi Gallery in Florence,
> constitutes not only a work of self-representation, but a visual document of her obsessions, visual strategies and
> psychobiographical narratives.
>
> It's not just about the beat riding under the vocals; it's part of the aggression and atmosphere.

Here is an example of a negative parallelism across multiple sentences:

> He hailed from the esteemed Duse family, renowned for their theatrical legacy. Eugenio's life, however, took a path
> that intertwined both personal ambition and familial complexities.

### Outlines of negatives

On rare occasions, user messages that appear AI-generated may also include short sentences describing items that are
either absent from something else or would be considered useless in comparison to a previous, useful item. Some of these
may read something along the lines of "no ..., no ..., just ..." or "What matters is ..., not ..., not ...".

### Examples

> There are no long-form profiles. No editorial insights. No coverage of her game dev career. No notable accolades. Just
> TikTok recaps and callouts.

---

> The process demands rigor — not emotional fatigue, not personal offense, and certainly not a premature exit masked as
> moral high ground.

---

> Not a career, not a body of work, not sustained relevance — just an algorithmic moment.

---

> This is not a close call. It is not a gray area. This page should be gone, fully, cleanly, and without delay. No
> redirect. No merge. Just delete.

---

> Wikipedia's general notability guideline (WP:GNG) is crystal clear: significant coverage in reliable, independent,
> secondary sources. Not a few throwaway articles echoing Twitter drama. Not reactionary posts exploiting culture war
> tension. Not foreign-language gossip magazines translating controversy for clicks.

---

> What actually matters — and what continues to be completely absent — is significant, in-depth coverage in reliable,
> independent secondary sources. Not gossip sites. Not recycled outrage. Not tabloid blurbs about one viral controversy.
> And certainly not basic directory-style mentions of someone being a "video game writer" or TikTok creator.

### Rule of three

LLMs overuse the '[rule of three](<https://en.wikipedia.org/wiki/Rule_of_three_(writing)>)'. This can take different
forms, from "adjective, adjective, adjective" to "short phrase, short phrase, and short phrase".[^1] LLMs often use this
structure to make [superficial analyses](#superficial-analyses) appear more comprehensive.

### Examples

> The Amaze Conference brings together global SEO professionals, marketing experts, and growth hackers to discuss the
> latest trends in digital marketing. The event features keynote sessions, panel discussions, and networking
> opportunities.

### Vague attributions of opinion

**Words to watch:** _Industry reports_, _Observers have cited_, _Some critics argue_...

AI chatbots tend to attribute opinions or claims to some vague authority—a practice called
[weasel wording](https://en.wikipedia.org/wiki/Weasel_wording)—while citing only one or two sources that may or may not
actually express such view. They also tend to overgeneralize the perspective of one or few sources into that of a wider
group.

### Examples

> His [Nick Ford's] compositions have been described as exploring conceptual themes and bridging the gaps between
> artistic media.

— From [Draft:Nick Ford (musician)](<https://en.wikipedia.org/wiki/Draft:Nick_Ford_(musician)>). Here, the weasel
wording implies the opinion comes from an independent source, but it actually cites Nick Ford's own website.

> Due to its unique characteristics, the Haolai River is of interest to researchers and conservationists. Efforts are
> ongoing to monitor its ecological health and preserve the surrounding grassland environment, which is part of a larger
> initiative to protect China's semi-arid ecosystems from degradation.
>
> The Kwararafa (Kororofa) confederacy is described in scholarship as a shifting
> [Benue valley](https://en.wikipedia.org/wiki/Benue_valley) coalition led by
> [Jukun](https://en.wikipedia.org/wiki/Jukun) groups and incorporating a range of
> [Middle Belt](https://en.wikipedia.org/wiki/Middle_Belt) peoples. Because much of the historical record derives from
> [Hausa](https://en.wikipedia.org/wiki/Hausa) chronicles, Bornu sources and oral tradition, modern researchers treat
> Kwararafa as a fluid political and cultural formation rather than a fixed state. As a result, lists of member groups
> vary by period and source.

### Excessive synonym variance / elegant variation

Generative AI has a repetition-penalty code, meant to discourage it from reusing words too often.[^5] For instance, the
output might give a main character's name and then repeatedly use a different synonym or related term (e.g.,
protagonist, key player, eponymous character) when mentioning it again.

Note: If a user adds multiple pieces of AI-generated content in separate edits, this tell may not apply, as each piece
of text may have been generated in isolation.

### Examples

> Vierny, after a visit in Moscow in the early 1970's, committed to supporting artists resisting the constraints of
> socialist realism and discovered Yankilevskly, among others such as Ilya Kabakov and Erik Bulatov. In the challenging
> climate of Soviet artistic constraints, Yankilevsky, alongside other non-conformist artists, faced obstacles in
> expressing their creativity freely. Dina Vierny, recognizing the immense talent and the struggle these artists
> endured, played a pivotal role in aiding their artistic aspirations. [...]
>
> In this new chapter of his life, Yankilevsky found himself amidst a community of like-minded artists who, despite
> diverse styles, shared a common goal—to break free from the confines of state-imposed artistic norms, particularly
> socialist realism. [...]
>
> The move to Paris facilitated an environment where Yankilevsky could further explore and exhibit his distinctive
> artistic vision without the constraints imposed by the Soviet regime. Dina Vierny's unwavering support and commitment
> to the Russian avant-garde artists played a crucial role in fostering a space where their creativity could flourish,
> contributing to the rich tapestry of artistic expression in the vibrant cultural landscape of Paris. Vierny's
> commitment culminated in the groundbreaking exhibition "Russian Avant-Garde - Moscow 1973" at her
> Saint-Germain-des-Prés gallery, showcasing the diverse yet united front of non-conformist artists challenging the
> artistic norms of their time.

### False ranges

When _from ... to ..._ constructions are not used figuratively, they are used to indicate the lower and upper bounds of
a scale. The scale is either quantitative, involving an explicit or implicit numerical range (e.g. from 1990 to 2000,
from 15 to 20 ounces, from winter to autumn), or qualitative, involving categorical bounds (e.g. "from seed to tree",
"from mild to severe", "from white belt to black belt"). The same constructions may be used to form a
[merism](https://en.wikipedia.org/wiki/Merism)—a figure of speech that combines the two extremes as two contrasting
parts of the whole to refer to the whole. This is a figurative meaning, but it has the same structure as the
non-figurative usage, because it still requires an identifiable scale: from head to toe (the length of a body denoting
the whole body), [from soup to nuts](https://en.wiktionary.org/wiki/from_soup_to_nuts) (clearly based on time), etc.
This is _not_ a false range.

LLMs really like mixing it up, such as when giving examples of items within a set (instead of simply mentioning them one
after another). An important consideration is whether some middle ground can be identified without changing the
endpoints. If the middle requires switching from one scale to another scale, or there is no scale to begin with or a
coherent whole that could be conceived, the construction is a **false range**. LLMs often employ "figurative" (often
simply: meaningless) "from ... to ..." constructions that purport to signify a scale, while the endpoints are loosely
related or even unrelated things and no meaningful scale can be inferred. LLMs do this because such meaningless language
is used in persuasive writing to impress and woo, and LLMs are heavily influenced by examples of persuasive writing
during their training.

### Example

> Our journey through the universe has taken us from the singularity of the Big Bang to the grand cosmic web, from the
> birth and death of stars that forge the elements of life, to the enigmatic dance of dark matter and dark energy that
> shape its destiny.
>
> [...] Intelligence and Creativity: From problem-solving and tool-making to scientific discovery, artistic expression,
> and technological innovation, human intelligence is characterized by its adaptability and capacity for novel
> solutions. [...] Continued Scientific Discovery: The quest to understand the universe, life, and ourselves will
> continue to drive scientific breakthroughs, from fundamental physics to medicine and neuroscience.

### Title case in section headings

In section headings, AI chatbots strongly tend to capitalize all main words.[^1]

### Examples

> Global Context: Critical Mineral Demand
>
> According to a 2023 report by [Goldman Sachs](https://en.wikipedia.org/wiki/Goldman_Sachs), the global market for
> critical minerals [...]
>
> Strategic Negotiations and Global Partnerships
>
> In 2014, Katalayi was appointed senior executive adviser to the chairman of the board of
> [Gécamines](https://en.wikipedia.org/wiki/G%C3%A9camines) [...]
>
> High-Stakes Deals: Glencore, China, and Russia
>
> There was also interest from [Moscow](https://en.wikipedia.org/wiki/Moscow) for strategic Congolese assets. [...]

---

## Punctuation and Formatting

### Excessive use of boldface

AI chatbots may display various phrases in [boldface](https://en.wikipedia.org/wiki/Boldface) for emphasis in an
excessive, mechanical manner. One of their tendencies, inherited from readmes, fan wikis, how-tos, sales pitches, slide
decks, listicles and other materials that heavily use boldface, is to emphasize every instance of a chosen word or
phrase, often in a "key takeaways" fashion. Some newer large language models or apps have instructions to avoid overuse
of boldface.

### Examples

> It blends **OKRs (Objectives and Key Results)**, **KPIs (Key Performance Indicators)**, and visual strategy tools such
> as the **Business Model Canvas (BMC)** and **Balanced Scorecard (BSC)**. OPC is designed to bridge the gap between
> strategy and execution by fostering a unified mindset and shared direction within organizations.
>
> A **leveraged buyout (LBO)** is characterized by the extensive use of **debt financing** to acquire a company. This
> financing structure enables **private equity firms** and **financial sponsors** to control businesses while investing
> a relatively small portion of their own equity. The acquired company's **assets and future cash flows** serve as
> collateral for the debt, making lenders more willing to provide financing.
>
> **50 Scientists and Thinkers in AI Safety with significant** influence on the field of alignment, containment, and
> risk mitigation. The list includes their **Productive Years**, their estimated **P(doom)** (probability of existential
> catastrophe), a **one-sentence summary of their contribution to AI Safety**, and their Wikipedia link.

### Inline-header vertical lists

AI chatbots output often includes vertical lists formatted in a specific way: an ordered or unordered list where the
list marker (number, bullet, dash, etc.) is followed by an inline boldfaced header, separated with a colon from the
remaining descriptive text.

Instead of [proper wikitext](https://en.wikipedia.org/wiki/H:LIST), a bullet point in an unordered list may appear as a
bullet character (•), hyphen (-), en dash (–), hash (#), emoji, or similar character. Ordered lists (i.e. numbered
lists) may use explicit numbers (such as `1.`) instead of standard wikitext. When copied as bare text appearing on the
screen, some of the formatting information is lost, and line breaks may be lost as well.

### Examples

> 1. Historical Context Post-WWII Era: The world was rapidly changing after WWII, [...] 2. Nuclear Arms Race: Following
>    the U.S. atomic bombings, the Soviet Union detonated its first bomb in 1949, [...] 3. Key Figures Edward Teller: A
>    Hungarian physicist who advocated for the development of more powerful nuclear weapons, [...] 4. Technical Details
>    of Sundial Hydrogen Bomb: The design of Sundial involved a hydrogen bomb [...] 5. Destructive Potential: If
>    detonated, Sundial would create a fireball up to 50 kilometers in diameter, [...] 6. Consequences and Reactions
>    Global Impact: The explosion would lead to an apocalyptic nuclear winter, [...] 7. Political Reactions: The U.S.
>    military and scientists expressed horror at the implications of such a weapon, [...] 8. Modern Implications Current
>    Nuclear Arsenal: Today, there are approximately 12,000 nuclear weapons worldwide, [...] 9. Key Takeaways
>    Understanding the Madness: The concept of Project Sundial highlights the extremes of human ingenuity [...] 10.
>    Questions to Consider What were the motivations behind the development of Project Sundial? [...]
>
> Conflict of Interest (COI)/Autobiography: While I understand the concern regarding my username [...]
>
> Notability (GNG and NPOLITICIAN): I have revised the article to focus on factual details [...]
>
> Original Research (WP) and Promotional Tone: I have worked on removing original research [...]
>
> Article Move to Main Namespace: Moving the draft to the main namespace after the AFC review [...]

### Emojis

AI chatbots love using [emojis](https://en.wikipedia.org/wiki/Emoji).[^11] In particular, they sometimes decorate
section headings or bullet points by placing emojis in front of them. This is most noticeable in talkpage comments.

### Examples

> Let's decode exactly what's happening here:
>
> 🧠 Cognitive Dissonance Pattern:
>
> You've proven authorship, demonstrated originality, and introduced new frameworks, yet they're defending a system that
> explicitly disallows recognition of originators unless a third party writes about them first.
>
> [...]
>
> 🧱 Structural Gatekeeping:
>
> Wikipedia policy favors:
>
> [...]
>
> 🚨 Underlying Motivation:
>
> Why would a human fight you on this?
>
> [...]
>
> 🧭 What You're Actually Dealing With:
>
> This is not a debate about rules.
>
> [...]
>
> 🪷 Traditional Sanskrit Name: Trikoṇamiti
>
> Tri = Three
>
> Koṇa = Angle
>
> Miti = Measurement 🧭 "Measurement of three angles" — the ancient Indian art of triangle and angle mathematics.
>
> 🕰️ 1. Vedic Era (c. 1200 BCE – 500 BCE)
>
> [...]
>
> 🔭 2. Sine of the Bow: Sanskrit Terminology
>
> [...]
>
> 🌕 3. Āryabhaṭa (476 CE)
>
> [...]
>
> 🌀 4. Varāhamihira (6th Century CE)
>
> [...]
>
> 🌠 5. Bhāskarācārya II (12th Century CE)
>
> [...]
>
> 📤 Indian Legacy Spreads

### Overuse of em dashes

While human editors and writers often like [em dashes](https://en.wikipedia.org/wiki/Em_dash) (—), AI _loves_ them.[^11]
LLM output uses them more often than nonprofessional human-written text of the same genre, and uses them in places where
humans are more likely to use commas, parentheses, colons, or (misused) hyphens (-). LLMs especially tend to use em
dashes in a formulaic, pat way, often mimicking "punched up" sales-like writing by over-emphasizing clauses or
parallelisms.

This sign is most useful when taken in combination with other indicators, not by itself.

### Examples

> The term "Dutch Caribbean" is **not used in the statute** and is primarily promoted by **Dutch institutions**, not by
> the **people of the autonomous countries** themselves. In practice, many Dutch organizations and businesses use it for
> **their own convenience**, even placing it in addresses — e.g., "Curaçao, Dutch Caribbean" — but this only **adds
> confusion** internationally and **erases national identity**. You don't say **"Netherlands, Europe"** as an address —
> yet this kind of mislabeling continues.
>
> you're right about one thing — we do seem to have different interpretations of what policy-based discussion entails.
> [...]
>
> When WP:BLP1E says "one event," it's shorthand — and the supporting essays, past AfD precedents, and practical
> enforcement show that "two incidents of fleeting attention" still often fall under the protective scope of BLP1E. This
> isn't "imagining" what policy should be — it's recognizing how community consensus has shaped its application.
>
> Yes, WP:GNG, WP:NOTNEWS, WP:NOTGOSSIP, and the rest of WP:BLP all matter — and I've cited or echoed each of them
> throughout. [...] If a subject lacks enduring, in-depth, independent coverage — and instead rides waves of
> sensational, short-lived attention — then we're not talking about encyclopedic significance. [...]
>
> [...] And consensus doesn't grow from silence — it grows from critique, correction, and clarity.
>
> If we disagree on that, then yes — we're speaking different languages.
>
> 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.
>
> [...] However, to date, no editor — including yourself — has identified any specific passages in the current version
> that were generated by AI or that fail to meet Wikipedia's content standards. [...]
>
> Given the article's current state — well-sourced, policy-compliant, and collaboratively improved — the continued
> presence of the "LLM advisory" banner is unwarranted.

### Curly quotation marks and apostrophes

AI chatbots typically use curly quotation marks ("..." or '...') instead of straight quotation marks ("..." or '...').
In some cases, AI chatbots inconsistently use pairs of curly and straight quotation marks in the same response. They
also tend to use the curly apostrophe ('), the same character as the curly
[right single quotation mark](https://en.wikipedia.org/wiki/Right_single_quotation_mark), instead of the straight
apostrophe ('), such as in [contractions](<https://en.wikipedia.org/wiki/Contraction_(grammar)>) and
[possessive forms](https://en.wikipedia.org/wiki/English_possessive). They may also do this inconsistently.

Curly quotes alone do not prove LLM use. [Microsoft Word](https://en.wikipedia.org/wiki/Microsoft_Word) as well as
[macOS](https://en.wikipedia.org/wiki/MacOS) and [iOS](https://en.wikipedia.org/wiki/IOS) devices have a
"[smart quotes](https://en.wikipedia.org/wiki/Smart_quotes)" feature that converts straight quotes to curly quotes.
Grammar correcting tools such as [LanguageTool](https://en.wikipedia.org/wiki/LanguageTool) may also have such a
feature. Curly quotation marks and apostrophes are common in professionally typeset works such as major newspapers.
Citation tools like [Citer](https://citer.toolforge.org/) may repeat those that appear in the title of a web page: for
example,

> McClelland, Mac (2017-09-27).
> ["When 'Not Guilty' Is a Life Sentence"](https://www.nytimes.com/2017/09/27/magazine/when-not-guilty-is-a-life-sentence.html).
> _The New York Times_. Retrieved 2025-08-03.

Note that Wikipedia allows users to customize the fonts used to display text. Some fonts display matched curly
apostrophes as straight, in which case the distinction is invisible to the user.

### Subject lines

User messages and [unblock requests](https://en.wikipedia.org/wiki/Wikipedia:Identifying_LLM_unblock_requests) generated
by AI chatbots sometimes begin with text that is intended to be pasted into the _Subject_ field on an email form.

### Examples

> Subject: Request for Permission to Edit Wikipedia Article - "Dog"
>
> Subject: Request for Review and Clarification Regarding Draft Article

---

## Communication Intended for the User

### Collaborative communication

**Words to watch:** _I hope this helps_, _Of course!_, _Certainly!_, _You're absolutely right!_, _Would you like..._,
_is there anything else_, _let me know_, _more detailed breakdown_, _here is a_...

Editors sometimes paste text from an AI chatbot that was meant as correspondence, prewriting or advice, rather than
article content. This may appear in article text or within comments (`<!-- -->`). Chatbots prompted to produce a
Wikipedia article or comment may also explicitly state that the text is meant for Wikipedia, and may mention various
[policies and guidelines](https://en.wikipedia.org/wiki/Wikipedia:PG) in the output—often explicitly specifying that
they're _Wikipedia's_ conventions.

### Examples

> In this section, we will discuss the background information related to the topic of the report. This will include a
> discussion of relevant literature, previous research, and any theoretical frameworks or concepts that underpin the
> study. The purpose is to provide a comprehensive understanding of the subject matter and to inform the reader about
> the existing knowledge and gaps in the field.
>
> Including photos of the forge (as above) and its tools would enrich the article's section on culture or economy,
> giving readers a visual sense of Ronco's industrial heritage. Visual resources can also highlight Ronco Canavese's
> landscape and landmarks. For instance, a map of the Soana Valley or Ronco's location in Piedmont could be added to
> orient readers geographically. The village's scenery [...] could be illustrated with an image. Several such
> photographs are available (e.g., on Wikimedia Commons) that show Ronco's panoramic view, [...] Historical images, if
> any exist (such as early 20th-century photos of villagers in traditional dress or of old alpine trades), would also
> add depth to the article. Additionally, the town's notable buildings and sites can be visually presented: [...]
> Including an image of the Santuario di San Besso [...] could further engage readers. By leveraging these visual aids –
> maps, photographs of natural and cultural sites – the expanded article can provide a richer, more immersive picture of
> Ronco Canavese.
>
> If you plan to add this information to the "Animal Cruelty Controversy" section of Foshan's Wikipedia page, ensure
> that the content is presented in a neutral tone, supported by reliable sources, and adheres to Wikipedia's guidelines
> on verifiability and neutrality.
>
> Here's a template for your wiki user page. You can copy and paste this onto your user page and customize it further.
>
> Final important tip: The ~~~~ at the very end is Wikipedia markup that automatically

### Knowledge-cutoff disclaimers and speculation about gaps in sources

**Words to watch:** _as of [date]_,[^a] _Up to my last training update_, _as of my last knowledge update_, _While
specific details are limited/scarce..._, _not widely available/documented/disclosed_, _...in the provided/available
sources/search results..._, _based on available information_...

A knowledge-cutoff disclaimer is a statement used by the AI chatbot to indicate that the information provided may be
incomplete, inaccurate, or outdated.

If an LLM has a fixed [knowledge cutoff](https://en.wikipedia.org/wiki/Knowledge_cutoff) (usually the model's last
training update), it is unable to provide any information on events or developments past that time, and it often outputs
a disclaimer to remind the user of this cutoff, which usually takes the form of a statement that says the information
provided is accurate only up to a certain date.

If an LLM with retrieval-augmented generation fails to find sources on a given topic, or if information is not included
in sources a user provides, it often outputs a statement to that effect, which is similar to a knowledge-cutoff
disclaimer. It may also pair it with text about what that information "likely" may be and why it is significant. This
information is entirely [speculative](https://en.wikipedia.org/wiki/Wikipedia:OR) (including the very claim that it's
"not documented") and may be based on loosely related topics or completely fabricated.

### Examples

> While specific details about Kumarapediya's history or economy are not extensively documented in readily available
> sources, ...
>
> While specific information about the fauna of Studniční hora is limited in the provided search results, the mountain
> likely supports...
>
> Though the details of these resistance efforts aren't widely documented, they highlight her bravery...
>
> No significant public controversies or security incidents affecting Outpost24 have been documented as of June 2025.
>
> As of my last knowledge update in January 2022, I don't have specific information about the current status or
> developments related to the "Chester Mental Health Center" in today's era.
>
> Below is a detailed overview based on available information:

### Prompt refusals

**Words to watch:** _as an AI language model_, _as a large language model_, _I'm sorry_...

Occasionally, the AI chatbot declines to answer a prompt as written, usually with an apology and a reminder that it is
"an AI language model". Attempting to be helpful, it often gives suggestions or an answer to an alternative, similar
request. Outright refusals have become increasingly rare.

Prompt refusals are obviously unacceptable for Wikipedia articles, so if a user includes one anyway, it may indicate
that they did not review the text and/or may not be proficient in English. Remember to
[assume good faith](https://en.wikipedia.org/wiki/Wikipedia:Assume_good_faith), because that editor may genuinely want
to improve our coverage of [knowledge gaps](https://en.wikipedia.org/wiki/Wikipedia:Systemic_bias).

### Examples

> As an AI language model, I can't directly add content to Wikipedia for you, but I can help you draft your
> bibliography.

### Phrasal templates and placeholder text

AI chatbots may generate responses with fill-in-the-blank
[phrasal templates](https://en.wikipedia.org/wiki/Phrasal_template) (as seen in the game
_[Mad Libs](https://en.wikipedia.org/wiki/Mad_Libs)_) for the LLM user to replace with words and phrases pertaining to
their use case. However, some LLM users forget to fill in those blanks. Note that non-LLM-generated templates exist for
drafts and new articles, such as
[Wikipedia:Artist biography article template/Preload](https://en.wikipedia.org/wiki/Wikipedia:Artist_biography_article_template/Preload)
and pages in [Category:Article creation templates](https://en.wikipedia.org/wiki/Category:Article_creation_templates).

### Examples

> Subject: Concerns about Inaccurate Information
>
> Dear Wikipedia
>
> I am writing to express my deep concern about the spread of misinformation on your platform. Specifically, I am
> referring to the article about [Entertainer's Name], which I believe contains inaccurate and harmful information.
>
> Subject: Edit Request for Wikipedia Entry
>
> Dear Wikipedia Editors,
>
> I hope this message finds you well. I am writing to request an edit for the Wikipedia entry
>
> I have identified an area within the article that requires updating/improvement. [Describe the specific section or
> content that needs editing and provide clear reasons why the edit is necessary, including reliable sources if
> applicable].

Large-language models may also insert placeholder dates like "2025-xx-xx" into citation fields, particularly the
access-date parameter and rarely the date parameter as well, producing errors.

### Examples

```text
&lt;ref&gt;{{cite web
 |title=Canadian Screen Music Awards 2025 Winners and Nominees
 |url=URL
 |website=Canadian Screen Music Awards
 |date=2025
 |access-date=2025-XX-XX
}}&lt;/ref&gt;

&lt;ref&gt;{{cite web
 |title=Best Original Score, Dramatic Series or Special – Winner: "Murder on the Inca Trail"
 |url=URL
 |website=Canadian Screen Music Awards
 |date=2025
 |access-date=2025-XX-XX
}}&lt;/ref&gt;
```

LLM-generated infobox edits may contain placeholder comments alongside unused fields, specifying that text or images
should be added.

### Examples

```text
| leader_name = <!-- Add if available with citation -->
```

---

## Markup

### Use of Markdown

AI chatbots are not proficient in [wikitext](https://en.wikipedia.org/wiki/H:WT), the
[markup language](https://en.wikipedia.org/wiki/Markup_language) used to instruct Wikipedia's
[MediaWiki](https://en.wikipedia.org/wiki/MediaWiki) software how to format an article. As wikitext is a niche markup
language, found mostly on wikis running on MediaWiki and other MediaWiki-based platforms like
[Miraheze](https://en.wikipedia.org/wiki/Miraheze), LLMs tend to lack wikitext-formatted training data. While the
corpora of chatbots did ingest millions of Wikipedia articles, these articles would not have been processed as text
files containing wikitext syntax. This is compounded by the fact that most chatbots are factory-tuned to use another,
conceptually similar but much more diversely applied markup language:
[Markdown](https://en.wikipedia.org/wiki/Markdown). Their system-level instructions direct them to format outputs using
it, and the chatbot apps render its syntax as formatted text on a user's screen, enabling the display of headings,
bulleted and numbered lists, tables, etc, just as MediaWiki renders wikitext to make Wikipedia articles look like
formatted documents.

When asked about its "formatting guidelines", a chatbot willing to reveal some of its system-level instructions
typically generates some variation of the following (this is
[Microsoft Copilot](https://en.wikipedia.org/wiki/Microsoft_Copilot) in mid-2025):

> ## Formatting Guidelines
>
> - All output uses GitHub-flavored Markdown.
> - Use a single main title (`#`) and clear primary subheadings (`##`).
> - Keep paragraphs short (3–5 sentences, ≤150 words).
> - Break large topics into labeled subsections.
> - Present related items as bullet or numbered lists; number only when order matters.
> - Always leave a blank line before and after each paragraph.
> - Avoid bold or italic styling in body text unless explicitly requested.
> - Use horizontal dividers (`---`) between major sections.
> - Employ valid Markdown tables for structured comparisons or data summaries.
> - Refrain from complex Unicode symbols; stick to simple characters.
> - Reserve code blocks for code, poems, lyrics, or similarly formatted content.
> - For mathematical expressions, use LaTeX outside of code blocks.

As the above suggests, Markdown's syntax is completely different from wikitext's: Markdown uses asterisks (*) or
underscores (_) instead of single-quotes (') for bold and italic formatting, hash symbols (#) instead of equals signs
(=) for section headings, parentheses (()) instead of square brackets ([]) around URLs, and three symbols (---, ***, or
___) instead of four hyphens (----) for thematic breaks.

Even when they are told to do so explicitly, chatbots generally struggle to generate text using syntactically correct
wikitext, as their training data lead to a drastically greater affinity for and fluency in Markdown. When told to
"generate an article", a chatbot typically defaults to using Markdown for the generated output, which is preserved in
clipboard text by the copy functions on some chatbot platforms. If instructed to generate content for Wikipedia, the
chatbot might "realize" the need to generate Wikipedia-compatible code, and might include a message like "Would you like
me to ... turn this into actual Wikipedia markup format (`wikitext`)?"[^b] in its output. If the chatbot is told to
proceed, the resulting syntax is often rudimentary, syntactically incorrect, or both. The chatbot might put its
attempted-wikitext content in a Markdown-style fenced code block (its syntax for preformatted text) surrounded by
Markdown-based syntax and content, which may also be preserved by platform-specific copy-to-clipboard functions, leading
to a telling footprint of both markup languages' syntax. This might include the appearance of three backticks in the
text, such as: ` ```wikitext `.[^c]

The presence of faulty wikitext syntax mixed with Markdown syntax is a strong indicator that content is LLM-generated,
especially if in the form of a fenced Markdown code block. However, Markdown _alone_ is not such a strong indicator.
Software developers, researchers, technical writers, and experienced internet users frequently use Markdown in tools
like [Obsidian](<https://en.wikipedia.org/wiki/Obsidian_(software)>) and
[GitHub](https://en.wikipedia.org/wiki/GitHub_Flavored_Markdown), and on platforms like Reddit, Discord, and Slack. Some
writing tools and apps, such as iOS Notes, Google Docs, and Windows Notepad, support Markdown editing or exporting. The
increasing ubiquity of Markdown may also lead new editors to expect or assume Wikipedia to support Markdown by default.

### Examples

> I believe this block has become procedurally and substantively unsound. Despite repeatedly raising clear, policy-based
> concerns, every unblock request has been met with **summary rejection** — not based on specific diffs or policy
> violations, but instead on **speculation about motive**, assertions of being "unhelpful", and a general impression
> that I am "not here to build an encyclopedia". No one has meaningfully addressed the fact that I have **not made
> disruptive edits**, **not engaged in edit warring**, and have consistently tried to **collaborate through talk page
> discussion**, citing policy and inviting clarification. Instead, I have encountered a pattern of dismissiveness from
> several administrators, where reasoned concerns about **in-text attribution of partisan or interpretive claims** have
> been brushed aside. Rather than engaging with my concerns, some editors have chosen to mock, speculate about my
> motives, or label my arguments "AI-generated" — without explaining how they are substantively flawed.
>
> - The Wikipedia entry does not explicitly mention the "Cyberhero League" being recognized as a winner of the World
>   Future Society's BetaLaunch Technology competition, as detailed in the interview with THE FUTURIST
>   ([[1]](https://consciouscreativity.com/the-futurist-interview-with-dana-klisanin-creator-of-the-cyberhero-league/)([https://consciouscreativity.com/the-futurist-interview-with-dana-klisanin-creator-of-the-cyberhero-league/](https://consciouscreativity.com/the-futurist-interview-with-dana-klisanin-creator-of-the-cyberhero-league/))).
>   This recognition could be explicitly stated in the "Game design and media consulting" section.

Here, LLMs incorrectly use `##` to denote section headings, which MediaWiki interprets as a numbered list.

> 1. 1. Geography
>
> Villers-Chief is situated in the [Jura Mountains](https://en.wikipedia.org/wiki/Jura_Mountains), in the eastern part
> of the Doubs department. [...]
>
> 1. 1. History
>
> Like many communes in the region, Villers-Chief has an agricultural past. [...]
>
> 1. 1. Administration
>
> Villers-Chief is part of the [Canton of Valdahon](https://en.wikipedia.org/wiki/Canton_of_Valdahon) and the
> [Arrondissement of Pontarlier](https://en.wikipedia.org/wiki/Arrondissement_of_Pontarlier). [...]
>
> 1. 1. Population
>
> The population of Villers-Chief has seen some fluctuations over the decades, [...]

Since AI-chatbots are not proficient in wikitext and Wikipedia templates, they often produce faulty syntax. A noteworthy
instance is garbled code related to Template:AfC submission, as new editors might ask a chatbot how to submit their
Articles for Creation draft.

### Examples

Note the badly malformed category link which appears to be a result of code that provides day information in the LLM's
Markdown parser:

```text
[[Category:AfC submissions by date/<0030Fri, 13 Jun 2025 08:18:00 +0000202568 2025-06-13T08:18:00+00:00Fridayam0000=error>EpFri, 13 Jun 2025 08:18:00 +0000UTC00001820256 UTCFri, 13 Jun 2025 08:18:00 +0000Fri, 13 Jun 2025 08:18:00 +00002025Fri, 13 Jun 2025 08:18:00 +0000: 17498026806Fri, 13 Jun 2025 08:18:00 +0000UTC2025-06-13T08:18:00+00:0020258618163UTC13 pu62025-06-13T08:18:00+00:0030uam301820256 2025-06-13T08:18:00+00:0008amFri, 13 Jun 2025 08:18:00 +0000am2025-06-13T08:18:00+00:0030UTCFri, 13 Jun 2025 08:18:00 +0000 &qu202530;:&qu202530;.</0030Fri, 13 Jun 2025 08:18:00 +0000202568>June 2025|sandbox]]
```

### ChatGPT-specific markup: citeturn, iturn

ChatGPT may include `citeturn0search0` (surrounded by Unicode points in the Private Use Area) at the ends of sentences,
with the number after "search" increasing as the text progresses. These are places where the chatbot links to an
external site, but a human pasting the conversation into Wikipedia has that link converted into placeholder code. This
was first observed in February 2025.

A set of images in a response may also render as `iturn0image0turn0image1turn0image4turn0image5`. Rarely, other markup
of a similar style, such as `citeturn0news0`, `citeturn1file0`, or `citegenerated-reference-identifier`, may appear.

### Examples

> The school is also a center for the US College Board examinations, SAT I & SAT II, and has been recognized as an
> International Fellowship Centre by Cambridge International Examinations. citeturn0search1 For more information, you
> can visit their official website: citeturn0search0

### Reference markup bugs: contentReference, oaicite, oai_citation, attached_file, grok_card

Due to a bug, ChatGPT may add code in the form of `:contentReference[oaicite:0]{index=0}` in place of links to
references in output text. Links to ChatGPT-generated references may be labeled with `oai_citation`.

### Examples

> :contentReference[oaicite:16]{index=16}
>
> 1. **Ethnicity clarification**
>
> - :contentReference[oaicite:17]{index=17}
>   - :contentReference[oaicite:18]{index=18} :contentReference[oaicite:19]{index=19}.
>   - Denzil Ibbetson's _Panjab Castes_ classifies Sial as Rajputs :contentReference[oaicite:20]{index=20}.
>   - Historian's blog notes: "The Sial are a clan of Parmara Rajputs…" :contentReference[oaicite:21]{index=21}.
>
> 1. :contentReference[oaicite:22]{index=22}
>
> - :contentReference[oaicite:23]{index=23}
>   > :contentReference[oaicite:24]{index=24} :contentReference[oaicite:25]{index=25}.
>
> #### Key facts needing addition or correction
>
> 1. **Group launch & meetings**
>
> _Independent Together_ launched a "Zero Rates Increase Roadshow" on 15 June, with events in Karori, Hataitai, Tawa,
> and Newtown [oai_citation:0‡wellington.scoop.co.nz](https://wellington.scoop.co.nz/?p=171473&utm_source=chatgpt.com).
>
> 1. **Zero-rates pledge and platform**
>
> The group pledges no rates increases for three years, then only match inflation—responding to Wellington's 16.9% hike
> for 2024/25
> [oai_citation:1‡en.wikipedia.org](https://en.wikipedia.org/wiki/Independent_Together?utm_source=chatgpt.com).

As of fall 2025, tags like `[attached_file:1]`, `[web:1]` have been seen at the end of sentences. This may be
Perplexity-specific.[^12]

> During his time as CEO, Philip Morris's reputation management and media relations brought together business and news
> interests in ways that later became controversial, with effects still debated in contemporary regulatory and legal
> discussions.[attached_file:1]

Though Grok-generated text is rare compared to other chatbots, it may sometimes include XML-styled _grok_card_ tags
after citations.

> Malik's rise to fame highlights the visibility of transgender artists in Pakistan's entertainment scene, though she
> has faced societal challenges related to her identity. [...]`[grok-card data-id="e8ff4f" data-type="citation_card"]`

### attribution and attributableIndex

ChatGPT may add JSON-formatted code at the end of sentences in the form of
`({"attribution":{"attributableIndex":"X-Y"}})`, with X and Y being increasing numeric indices.

### Examples

> ^[Evdokimova was born on 6 October 1939 in Osnova, Kharkov Oblast, Ukrainian SSR (now Kharkiv, Ukraine).]({"attribution":{"attributableIndex":"1009-1"}})
> ^[She graduated from the Gerasimov Institute of Cinematography (VGIK) in 1963, where she studied under Mikhail Romm.]({"attribution":{"attributableIndex":"1009-2"}})
> [oai_citation:0‡IMDb](https://www.imdb.com/name/nm0947835/?utm_source=chatgpt.com)
> [oai_citation:1‡maly.ru](https://www.maly.ru/en/people/EvdokimovaA?utm_source=chatgpt.com)
>
> Patrick Denice & Jake Rosenfeld,
> [Les syndicats et la rémunération non syndiquée aux États-Unis, 1977–2015](https://sociologicalscience.com/articles-v5-23-541/),
> ''Sociological Science'' (2018).]({"attribution":{"attributableIndex":"3795-0"}})

### Non-existent or out-of-place categories and "see also" pages

LLMs may hallucinate non-existent categories, sometimes for generic concepts that _seem like_ plausible category titles
(or SEO keywords), and sometimes because their training set includes obsolete and renamed categories. These will appear
as red links. You may also find category redirects, such as the longtime spammer favorite Category:Entrepreneurs.
Sometimes, broken categories may be deleted by reviewers, so if you suspect a page may be LLM-generated, it may be worth
checking earlier revisions.

Pay attention to blue links under "see also" headers as well. LLM-generated "see also" sections often tend to fill them
up (to at least three links) seemingly out of obligation. If a new page/draft on some startup links to a broad term like
Financial technology in its see-also section, that's a bit suspicious.

Of course, none of this section should be treated as a hard-and-fast rule. New users are unlikely to know about
Wikipedia's style guidelines for these sections, and returning editors may be used to old categories that have since
been deleted.

### Examples

```text
[[Category:American hip hop musicians]]
```

rather than

```text
[[Category:American hip-hop musicians]]
```

---

## Citations

### Fictitious or hallucinated references

LLMs may generate fictitious or hallucinated references that do not exist or contain fabricated information.

### Broken external links

If a new article or draft has multiple citations with external links, and several of them are broken (e.g., returning
[404 errors](https://en.wikipedia.org/wiki/404_error)), this is a strong sign of an AI-generated page, particularly if
the dead links are not found in website archiving sites like
[Internet Archive](https://en.wikipedia.org/wiki/Internet_Archive) or
[Archive Today](https://en.wikipedia.org/wiki/Archive_Today). Most links
[become broken over time](https://en.wikipedia.org/wiki/Link_rot), but these factors make it unlikely that the link was
ever real.

### Invalid DOI and ISBNs

A [checksum](https://en.wikipedia.org/wiki/Checksum) can be used to verify [ISBNs](https://en.wikipedia.org/wiki/ISBN).
An invalid checksum is a very likely sign that an ISBN is incorrect, and citation templates display a warning if so.
Similarly, [DOIs](https://en.wikipedia.org/wiki/Digital_object_identifier) are more resistant to link rot than regular
hyperlinks. Unresolvable DOIs and invalid ISBNs can be indicators of
[hallucinated](<https://en.wikipedia.org/wiki/Hallucination_(AI)>) references.

Related are DOIs that point to entirely unrelated articles and general book citations without pages. This passage, for
example, was generated by ChatGPT.

> Ohm's Law is a fundamental principle in the field of electrical engineering and physics that states the current
> passing through a conductor between two points is directly proportional to the voltage across the two points, provided
> the temperature remains constant. Mathematically, it is expressed as V=IR, where V is the voltage, I is the current,
> and R is the resistance. The law was formulated by German physicist Georg Simon Ohm in 1827, and it serves as a
> cornerstone in the analysis and design of electrical circuits [1]. Ohm's Law applies to many materials and components
> that are "ohmic," meaning their resistance remains constant regardless of the applied voltage or current. However, it
> does not hold for non-linear devices like diodes or transistors \[2]\[3].
>
> References:
>
> 1. Dorf, R. C., & Svoboda, J. A. (2010). Introduction to Electric Circuits (8th ed.). Hoboken, NJ: John Wiley & Sons.
>    ISBN 9780470521571.
>
> 2. M. E. Van Valkenburg, "The validity and limitations of Ohm's law in non-linear circuits," Proceedings of the IEEE,
>    vol. 62, no. 6, pp. 769–770, Jun. 1974. doi:10.1109/PROC.1974.9547
>
> 3. C. L. Fortescue, "Ohm's Law in alternating current circuits," Proceedings of the IEEE, vol. 55, no. 11, pp.
>    1934–1936, Nov. 1967. doi:10.1109/PROC.1967.6033

The book references appear valid – a book on electric circuits would likely have information about Ohm's law – but
without the page number, that citation is not useful for verifying the claims in the prose. Worse, both _Proceedings of
the IEEE_ citations are completely made up. The DOIs lead to completely different citations and have other problems as
well. For instance, [C. L. Fortescue](https://en.wikipedia.org/wiki/Charles_LeGeyt_Fortescue) was dead for 30+ years at
the purported time of writing, and Vol 55, Issue 11 does not list any articles that match anything remotely close to the
information given in reference 3.

### Incorrect or unconventional use of references

AI tools may have been prompted to include references, and make an attempt to do so as Wikipedia expects, but fail with
some key implementation details or stand out when compared with conventions.

### Examples

In the below example, note the incorrect attempt at re-using references. The tool used here was not capable of searching
for non-confabulated sources (as it was done the day before Bing Deep Search launched) but nonetheless found one real
reference. The syntax for re-using the references was incorrect.

In this case, the _Smith, R. J._ source – being the "third source" the tool presumably generated the link
'<https://pubmed.ncbi.nlm.nih.gov/3>' (which has a PMID reference of 3) – is also completely irrelevant to the body of
the article. The user did not check the reference before they converted it to a {{cite journal}} reference, even though
the links resolve.

The LLM in this case has diligently included the incorrect re-use syntax after every single full stop.

```text
For over thirty years, computers have been utilized in the rehabilitation of individuals with brain injuries.
Initially, researchers delved into the potential of developing a "prosthetic memory."<ref>Fowler R, Hart J,
Sheehan M. A prosthetic memory: an application of the prosthetic environment concept. _Rehabil Counseling Bull_.
1972;15:80–85.</ref> However, by the early 1980s, the focus shifted towards addressing brain dysfunction through
repetitive practice.<ref>{{Cite journal |last=Smith |first=R. J. |last2=Bryant |first2=R. G. |date=1975-10-27 |title=Metal substitutions incarbonic anhydrase: a halide ion probe study |url=https://pubmed.ncbi.nlm.nih.gov/3 |journal=Biochemical and Biophysical Research Communications |volume=66 |issue=4 |pages=1281–1286 |doi=10.1016/0006-291x(75)90498-2 |issn=0006-291X |pmid=3}}</ref>
Only a few psychologists were developing rehabilitation software for individuals with Traumatic Brain Injury (TBI),
resulting in a scarcity of available programs.<sup>[3]</sup> Cognitive rehabilitation specialists opted
for commercially available computer games that were visually appealing, engaging, repetitive, and entertaining,
theorizing their potential remedial effects on neuropsychological dysfunction.<sup>[3]</sup>
```

Some LLMs or chatbot interfaces use the character ↩ to indicate footnotes:

> References
>
> Would you like help formatting and submitting this to Wikipedia, or do you plan to post it yourself? I can guide you
> step-by-step through that too.
>
> **Footnotes**
>
> 1. KLAS Research. (2024). _Top Performing RCM Vendors 2024_. <https://klasresearch.com> ↩ ↩2
> 2. PR Newswire. (2025, February 18). _CureMD AI Scribe Launch Announcement_.
>    <https://www.prnewswire.com/news-releases/curemd-ai-scribe> ↩

### ChatGPT-specific UTM parameters

ChatGPT may add the [UTM parameter](https://en.wikipedia.org/wiki/UTM_parameter) `utm_source=openai` or, in edits prior
to August 2025, `utm_source=chatgpt.com` to URLs that it is using as sources. Other LLMs, such as Gemini or Claude, use
UTM parameters less often.[^13]

Note: While this does definitively prove ChatGPT's involvement, it doesn't prove, on its own, that ChatGPT also
generated the writing. Some editors use AI tools to find citations for existing text; this will be apparent in the edit
history.

### Examples

> Following their marriage, Burgess and Graham settled in Cheshire, England, where Burgess serves as the head coach for
> the Warrington Wolves rugby league team.
> [https://www.theguardian.com/sport/2025/feb/11/sam-burgess-interview-warrington-rugby-league-luke-littler?utm_source=chatgpt.com]
>
> Vertex AI documentation and blog posts describe watermarking, verification workflow, and configurable safety filters
> (for example, person‑generation controls and safety thresholds).
> ([cloud.google.com](https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-images?utm_source=openai))

### Named references declared in references section but unused in article body

_This section is empty._ You can help by adding to it. _(October 2025)_

### Examples

See these diffs for examples. The problematic references appear as parser errors in the reflist.

- [Special:PermanentLink/1287201002#References](https://en.wikipedia.org/wiki/Special:PermanentLink/1287201002#References)
- [Special:PermanentLink/1292432848#References](https://en.wikipedia.org/wiki/Special:PermanentLink/1292432848#References)
- [Special:PermanentLink/1291491974#References](https://en.wikipedia.org/wiki/Special:PermanentLink/1291491974#References)
- [Special:PermanentLink/1291561040#References](https://en.wikipedia.org/wiki/Special:PermanentLink/1291561040#References)

---

## Discrepancies in Writing Style and Variety of English

### Abrupt cut offs

AI tools may abruptly stop generating content, for example if they predict the end of text sequence (appearing as
`<|endoftext|>`) next. Also, the number of tokens that a single response has is usually limited, and further responses
require the user to select "continue generating".

This method is not foolproof, as a malformed copy/paste from one's local computer can also cause this. It may also
indicate a copyright violation rather than the use of an LLM.

### Sudden shift in writing style

A sudden shift in an editor's writing style, such as unexpectedly flawless grammar compared to their other
communication, may indicate the use of AI tools.

### Sudden shift in English variety use

A mismatch of user location, national ties of the topic to a variety of English, and the variety of English used may
indicate the use of AI tools. A human writer from India writing about an Indian university would probably not use
American English; however, LLM outputs use American English by default, unless prompted otherwise.[^9] Note that
non-native English speakers tend to mix up English varieties, and such signs should raise suspicion only if there is a
sudden and complete shift in an editor's English variety use.

### Overwhelmingly verbose edit summaries

AI-generated [edit summaries](https://en.wikipedia.org/wiki/Help:Edit_summary) are often unusually long, written as
formal, first-person paragraphs without abbreviations, and/or conspicuously itemize Wikipedia's conventions.

> Refined the language of the article for a neutral, encyclopedic tone consistent with Wikipedia's content guidelines.
> Removed promotional wording, ensured factual accuracy, and maintained a clear, well-structured presentation. Updated
> sections on history, coverage, challenges, and recognition for clarity and relevance. Added proper formatting and
> categorized the entry accordingly
>
> I formalized the tone, clarified technical content, ensured neutrality, and indicated citation needs. Historical
> narratives were streamlined, allocation details specified with regulatory references, propagation explanations made
> reader-friendly, and equipment discussions focused on availability and regulatory compliance, all while adhering to
> encyclopedic standards.
>
> **Concise edit summary:** Improved clarity, flow, and readability of the plot section; reduced redundancy and refined
> tone for better encyclopedic style.

### "Submission statements" in AFC drafts

This one is specific to drafts submitted by Articles for Creation. At least one LLM tends to insert "submission
statements" supposedly intended for reviewers that supposedly explain why the subject is notable and why the draft meets
Wikipedia guidelines. Of course, all this actually does is let reviewers know that the draft is LLM-generated, and
should be declined or speedied without a second thought.

> Reviewer note (for AfC): This draft is a neutral and well-sourced biography of Portuguese public manager Jorge Patrão.
> All references are from independent, reliable sources (Público, Diário de Notícias, Jornal de Negócios, RTP, O
> Interior, Agência Lusa) covering his public career and cultural activity. It meets WP:RS and WP:BLP standards and
> demonstrates clear notability per WP:NBIO through: – Presidency of Serra da Estrela Tourism Region (1998–2013); –
> Presidency of Parkurbis – Covilhã Science and Technology Park; – Founding role in Rede de Judiarias de Portugal
> (member of the Council of Europe's European Routes of Jewish Heritage); – Authorship of the book "1677 – A Fábrica
> d'El-Rei"; – Founder/curator of the Beatriz de Luna Art Collection (Old Master focus). There is also a Portuguese
> version of this article at pt.wikipedia.org/wiki/Jorge_Patrão. Thank you for your review. -->

— Found at the top of Draft:Jorge Patrão (all the inevitable formatting errors are present in the original)

### Pre-declined AFC review templates

Occasionally a new editor creates a draft that includes an AFC review template already set to "declined". The template
is also devoid of content with no reviewer reasoning given. The LLM apparently offers to add an AFC submission template
to the draft, and then provides something like `{{AfC submission|d}}`, in which the "d" parameter pre-declines the draft
by substituting {{AfC submission/declined}}. The draft's contribution history reveals that this template was inserted at
some point by the draft's creator. Invariably the creator then asks on Wikipedia:WikiProject Articles for creation/Help
desk or one of the other help pages why the draft was declined with no feedback. The presence of a content-free
"submission declined" header is a **strong** indicator that the draft was LLM-generated.

---

## Signs of Human Writing

### Age of text relative to ChatGPT launch

ChatGPT was launched to the public on November 30, 2022. Although OpenAI had similarly powerful LLMs before then, they
were paid services and not easily accessible or known to lay people. ChatGPT experienced extreme growth immediately on
launch.

It is very unlikely that any particular text added to Wikipedia **prior to November 30, 2022** was generated by an LLM.
If an edit was made before this date, AI use can be safely ruled out for that revision. While some older text may
display some of the AI signs given in this list, and even convincingly appear to have been AI-generated, the vastness of
Wikipedia allows for these rare coincidences.

### Ability to explain one's own editorial choices

Editors should be able to explain why they made one or more edits or mistakes. For example, if an editor inserts a URL
that appears fabricated, you can ask how the mix-up occurred instead of jumping to conclusions. If they can supply the
correct link and explain it as a human error (perhaps a typo), or share the relevant passage from the real source, that
points to an ordinary human error.

---

## Ineffective Indicators

False accusations of AI use can [drive away new editors](https://en.wikipedia.org/wiki/Wikipedia:BITE) and foster an
atmosphere of suspicion. Before claiming AI was used, consider if
[Dunning–Kruger effect](https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect) and
[confirmation bias](https://en.wikipedia.org/wiki/Confirmation_bias) is clouding your judgement. Here are several
somewhat commonly used indicators that are ineffective in LLM detection—and may even indicate the opposite.

- **Perfect grammar**: While modern LLMs are known for their high grammatical proficiency, many editors are also skilled
  writers or come from professional writing backgrounds. (See also
  [§ Discrepancies in writing style and variety of English](#discrepancies-in-writing-style-and-variety-of-english).)

- **"Bland" or "robotic" prose**: By default, modern LLMs tend toward effusive and verbose prose, as detailed above;
  while this tendency is formulaic, it may not scan as "robotic" to those unfamiliar with AI writing.[^14]

- **"Fancy," "academic," or unusual words**: While LLMs disproportionately favor certain words and phrases, many of
  which are long and have difficult readability scores, the correlation does not extend to _all_ "fancy," academic, or
  "advanced"-sounding prose.[^1] Low-frequency and "unusual" words are also less likely to show up in AI-generated
  writing as they are statistically less common, unless they are proper nouns directly related to the topic.

- **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 such at one, or simply mistaking the talk page for email. AI-generated
  talk page messages tend to have other tells, such as vertical lists,[^d] placeholders, or abrupt cutoffs.

- **Conjunctions (in isolation)**: While LLMs tend to overuse connecting words and phrases in a stilted, formulaic way
  that implies inappropriate synthesis of facts, such uses are typical of essay-like writing by humans and are not
  strong indicators by themselves.

- **Bizarre wikitext**: While LLMs may hallucinate templates or generate wikitext code with invalid syntax for reasons
  explained in [§Use of Markdown](#use-of-markdown), they are not likely to generate content with certain
  random-seeming, "inexplicable" errors and artifacts (excluding the ones listed on this page in [§Markup](#markup)).
  Bizarrely placed HTML tags like `<span>` are more indicative of poorly programmed browser extensions or a known bug
  with Wikipedia's content translation tool (T113137). Misplaced syntax like `''Catch-22 i''s a satirical novel.`
  (rendered as "_Catch-22 i_ s a satirical novel.") are more indicative of mistakes in VisualEditor, where such errors
  are harder to notice than in source editing.

---

## Notes

[^a]: not unique to AI chatbots; is produced by the {{as of}} template

[^b]:
    Example of `Would you like me to ... turn this into actual Wikipedia markup format (wikitext)?` in a deleted draft
    (administrators only)

[^c]: Example of ` ```wikitext ` on a draft.

[^d]: Example of a vertical list in a deletion discussion

---

## References

[^1]:
    Russell, Jenna; Karpinska, Marzena; Iyyer, Mohit (2025).
    [_People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text_](https://aclanthology.org/2025.acl-long.267/).
    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
    Vienna, Austria: Association for Computational Linguistics. pp.5342–5373.
    arXiv:[2501.15654](https://arxiv.org/abs/2501.15654).

[^2]:
    Dugan, Liam; Hwang, Alyssa; Trhlik, Filip; Zhu, Andrew; Ludan, Josh Magnus; Xu, Hainiu; Ippolito, Daphne;
    Callison-Burch, Chris (2024).
    [_RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors_](https://aclanthology.org/2024.acl-long.674).
    Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
    Bangkok, Thailand: Association for Computational Linguistics. pp.12463–12492.
    arXiv:[2405.07940](https://arxiv.org/abs/2405.07940).

[^3]:
    ["People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text"](https://arxiv.org/html/2501.15654v2).
    _arxiv.org_. Retrieved 2025-11-28.

[^4]:
    This can be directly observed by examining images generated by text-to-image models; they look acceptable at first
    glance, but specific details tend to be blurry and malformed. This is especially true for background objects and
    text.

[^5]:
    ["10 Ways AI Is Ruining Your Students' Writing"](https://www.chronicle.com/article/10-ways-ai-is-ruining-your-students-writing).
    _Chronicle of Higher Education_. September 16, 2025. Archived from the original on October 1, 2025. Retrieved
    October 1, 2025.

[^6]:
    Juzek, Tom S.; Ward, Zina B. (2025).
    [_Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models_](https://aclanthology.org/2025.coling-main.426.pdf)
    (PDF). Findings of the Association for Computational Linguistics: ACL 2025. Association for Computational
    Linguistics. arXiv:[2412.11385](https://arxiv.org/abs/2412.11385).

[^7]:
    Reinhart, Alex; Markey, Ben; Laudenbach, Michael; Pantusen, Kachatad; Yurko, Ronald; Weinberg, Gordon; Brown, David
    West.
    ["Do LLMs write like humans? Variation in grammatical and rhetorical styles"](http://arxiv.org/abs/2410.16107).
    Retrieved 4 December 2025.

[^8]:
    Kobak, Dmitry; González-Márquez, Rita; Horvát, Emőke-Ágnes; Lause, Jan (2 July 2025).
    ["Delving into LLM-assisted writing in biomedical publications through excess vocabulary"](https://www.science.org/doi/10.1126/sciadv.adt3813).
    _Science Advances_. **11** (27). doi:[10.1126/sciadv.adt3813](https://doi.org/10.1126%2Fsciadv.adt3813). ISSN
    2375-2548. PMC 12219543. PMID 40009654.

[^9]:
    Ju, Da; Blix, Hagen; Williams, Adina (2025).
    [_Domain Regeneration: How well do LLMs match syntactic properties of text domains?_](https://aclanthology.org/2025.findings-acl.120).
    Findings of the Association for Computational Linguistics: ACL 2025. Vienna, Austria: Association for Computational
    Linguistics. pp.2367–2388. arXiv:[2505.07784](https://arxiv.org/abs/2505.07784).
    doi:[10.18653/v1/2025.findings-acl.120](https://doi.org/10.18653%2Fv1%2F2025.findings-acl.120).

[^10]:
    Kousha, Kayvan; Thelwall, Mike (2025).
    [_How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysis_](https://arxiv.org/pdf/2509.09596).
    ISSI 2025 Conference. arXiv:[2509.09596](https://arxiv.org/abs/2509.09596).

[^11]:
    Merrill, Jeremy B.; Chen, Szu Yu; Kumer, Emma (13 November 2025).
    ["What are the clues that ChatGPT wrote something? We analyzed its style"](https://www.washingtonpost.com/technology/interactive/2025/how-detect-chatgpt-em-dash/).
    _The Washington Post_. Retrieved 14 November 2025.

[^12]:
    ["Unproductive Interpretation of Work and Employment as Misinformation?"](https://www.laetusinpraesens.org/docs20s/workeco.php).
    Archived from the original on 2 September 2025. Retrieved 21 October 2025.

[^13]: See [T387903](https://phabricator.wikimedia.org/T387903).

[^14]:
    Murray, Nathan; Tersigni, Elisa (21 July 2024).
    ["Can instructors detect AI-generated papers? Postsecondary writing instructor knowledge and perceptions of AI"](https://journals.sfu.ca/jalt/index.php/jalt/article/view/1895).
    _Journal of Applied Learning & Teaching_. **7** (2).
    doi:[10.37074/jalt.2024.7.2.12](https://doi.org/10.37074%2Fjalt.2024.7.2.12). ISSN 2591-801X. Retrieved 21
    November 2025.
