AI Words and Phrases: The Full List, and Why Models Reach for Them

In 2024, at least one in eight biomedical papers indexed on PubMed carried the lexical signature of AI assistance, and in some countries and journals the share ran above two in five. Nobody had to admit anything. A handful of formal, faintly theatrical words did the confessing, appearing together in one abstract far more often than chance would allow.

AI words and phrases are not exotic. They are ordinary, formal English, reached for slightly too often, in combinations a careful writer would rarely stack inside one paragraph. Every general-purpose model produces them, not one brand: the pattern comes from how these systems are built and tuned, so the same vocabulary turns up whichever assistant drafted the page. This is the full working reference: more than forty single words, fifty stock phrases with their one-word replacements, the research explaining why models favour this exact vocabulary, and the editing procedure for clearing it out of a finished manuscript.

Why AI Reaches for the Same Words

A language model builds a sentence by picking the statistically likely next word, over and over, inside a system trained to sound competent rather than distinctive. Averaged across billions of parameters, the safe formal word beats the specific one nearly every time, which is why output from different assistants converges on one register. That habit shows up across word choice, sentence rhythm, punctuation and structure, covered in full in TextPulse's guide to AI writing patterns. Word choice is simply the level a reader notices first, because it is the easiest to point to on the page.

The second half of the explanation is the tuning stage rather than the training data, and it has been tested directly. Tom Juzek and Zina Ward, linguists at Florida State University, published at COLING 2025 after ruling out model architecture, algorithm choices and training data as sufficient explanations on their own. They then compared a base version of Meta's Llama model against a version trained on human preference data, the reinforcement learning from human feedback stage that tunes a model's tone. The preference-trained version reacted with far less surprise to abstracts already loaded with this vocabulary, which is consistent with human feedback nudging models toward it. Their own follow-up complicated the story: when they tested how people rated sentences opening with one of these words, reactions ran more negative than for the others, which does not fit a simple account where evaluators simply liked the word every time it appeared.

So the honest version is two-part and partly open. The register was already there in the edited, published prose these systems learned from, and the feedback stage appears to reinforce it. Word choice earns its own reference for one practical reason: it is checkable. A reader does not need a detector subscription to notice that a paragraph leans on five or six formal, low-information words at once. Counting them is a skill anyone can learn in the time it takes to read this page once.

The Full List of AI Words

The list below runs to more than forty entries, grouped by where each word tends to surface: academic and research prose, business and career writing, transitional stock phrases, and hedges that close a paragraph without saying much. Roughly half trace to a single large study of biomedical abstracts; the rest are documented informally, for instance a detection vendor's own scan of more than ten million words of ChatGPT output, or a November 2025 analysis that clocked the phrase "notable works include" at around 120 times its normal rate in AI-generated text and "today's fast-paced world" at roughly 107 times. The two kinds of source are worth knowing apart: a peer-reviewed sample of fifteen million abstracts carries different weight than one analyst's scan of chatbot output, and the column below does not pretend otherwise.

Word or phrase	Where it clusters	What a careful writer uses instead
delve (into)	Academic	look at, examine
underscore / underscores	Academic	point to, show
showcase / showcasing	Academic	present, demonstrate
intricate	Academic	complex, detailed
meticulous	Academic	careful, thorough
comprehensive	Academic	full, complete
notably	Academic	in particular
crucial	Academic	important, key
pivotal	Academic	central, key
realm	Academic	area, field
lens / through the lens of	Academic	viewpoint; in terms of
exhibited	Academic	showed
multifaceted	Academic	many-sided
encompasses	Academic	covers, includes
facilitates	Academic	helps, allows
elucidates	Academic	explains
discernible	Academic	visible, clear
underpinning	Academic	supporting
insights	Academic	findings, points
within	Academic	in
across	Academic	in, throughout
particularly	Academic	especially
noteworthy	Academic	worth mentioning
leverage	Business	use
utilize	Business	use
streamline	Business	simplify
elevate	Business	improve, raise
unlock	Business	open up, make possible
seamlessly	Business	smoothly, without extra steps
cutting-edge	Business	new, recent
robust	Business	solid, reliable
innovative	Business	new
results-driven	Business	effective
actionable	Business	usable, practical
groundbreaking	Business	new, original
transformative	Business	significant, major
state-of-the-art	Business	latest, newest
additionally	Transition	also, and
furthermore	Transition	also, and
moreover	Transition	also
in order to	Transition	to
due to the fact that	Transition	because
a wide range of	Transition	several, or the actual number
today's fast-paced world	Hedge / closer	name the actual year or field
notable works include	Hedge / closer	name the two or three that matter
aims to explore	Hedge / closer	examines, tests
it is worth noting	Hedge / closer	state the fact directly
plays a vital role in	Hedge / closer	matters to, shapes
serves as a testament to	Hedge / closer	shows, proves
boasts	Hedge / closer	has, includes

"Delve" gets one line above like everything else, though it is the most documented word on the list and has a section of its own further down. A faster check than reading the whole table by eye is running a draft through TextPulse's free AI word cleaner, which flags this kind of density automatically.

A Worked Example

Take one sentence carrying four entries from the table at once: "This comprehensive study aims to explore the multifaceted challenges that underscore the pivotal role of policy in modern research." Every word is real and every clause is grammatical, which is exactly the problem. A human editor would cut it to something like: "This study looks at how policy shapes research, and why it matters." Nothing factual was lost. What went missing was the padding.

Why AI "Underscores" Everything

Academic register carries the clearest evidence. In a study of 15.1 million PubMed abstracts published between 2010 and 2024, "underscores" rose to roughly 14 times its expected rate by 2024, and "showcasing" to nearly 11 times. Even ordinary words moved: "potential", "findings" and "crucial" all became measurably more common, just from a higher starting point, so the jump looks smaller as a raw ratio despite being real. The same research team singled out a cluster of ten otherwise unremarkable connectors, among them "additionally", "comprehensive", "notably" and "within", that together became statistically unusual as a group: no single one of them means anything alone, but four or five of them in one abstract does.

The same pattern shows up outside biomedicine, at different intensities by field. A separate analysis of roughly 951,000 papers on arXiv, bioRxiv and in Nature-family journals between January 2020 and February 2024 put likely LLM involvement as high as 17.5 percent in computer science, against a ceiling closer to 6.3 percent in mathematics and in the Nature portfolio. A humanities supervisor and a computer science one are not reading the same background rate of these words, which is worth remembering before treating any single essay as an outlier. Shorter papers and papers from authors who post frequent preprints showed the pattern more often too, which fits a simple explanation: the words save time, and time pressure is not evenly distributed across a discipline.

The business and career column above is documented more loosely, but no less visibly. "Aims to explore" turned up at roughly 50 times its normal rate in the same November 2025 analysis cited earlier, and cover letters, LinkedIn summaries and press releases are where "streamline", "elevate" and "results-driven" tend to cluster. A hiring manager reading forty near-identical cover letters in an afternoon notices this faster than any study measures it, which is why recruiters were describing the pattern anecdotally well before anyone ran the numbers. None of this proves a given cover letter was drafted by a chatbot. It proves the vocabulary of corporate writing and the vocabulary a language model reaches for overlap heavily, which was already true before ChatGPT existed and simply became easier to spot once one tool started producing so much of it at once.

The Case of "Delve": One Word, Measured

In April 2024, the writer and investor Paul Graham read a cold email, noticed it used the word "delve," and posted that this alone told him a chatbot had written it. The reaction reached far beyond his usual readers within days, and the loudest pushback came from Nigeria, where "delve" is ordinary, formal English: common in newspapers, in business writing, in prepared speech, used for years by people who had never opened ChatGPT.

The measurement behind the reputation is the sharpest on this page. In 2024, "delve" was running at about 28 times its pre-2023 rate, the fastest growth of any word they measured. Dmitry Kobak and three co-authors scoured 15.1 million English-language PubMed abstracts from 2010 to 2024, keeping tabs on which words appeared most often over time. They published their results in Science Advances in July 2025, estimating that at least 13.5 percent of 2024 abstracts carry this kind of vocabulary evidence of AI assistance, rising above 40 percent in some countries and journals.

That is not a word drifting slowly into fashion. It is a word uncommon enough in 2022 that its sudden frequency became something researchers could actually measure, and distinctive enough that ordinary readers started noticing it without needing a study to tell them so.

The Regional-English Theory, and What Testing It Found

A third explanation circulates constantly and deserves care, because it is the one most likely to cause harm if stated too confidently. Shortly after "delve" started attracting attention, the technology journalist Alex Hern proposed that OpenAI's reliance on outsourced human feedback workers, many based in Nigeria, might explain the pattern, reasoning that "delve" sits more comfortably in Nigerian formal and business English than in British or American usage. The theory travelled fast, and casual writing still repeats it as settled fact.

It is not settled. Juzek and Ward tested it directly, checking whether "delve" and twenty other flagged words were disproportionately common in any single variety of English represented in the International Corpus of English. They report finding no such evidence for any of the words tested, including "delve." That does not close the question: a single corpus, however carefully built, is a limited window onto how a word is actually used in everyday formal speech across a whole region, and the researchers themselves treat the wider puzzle as open rather than solved. What can be said responsibly is narrower than the popular version: a specific regional explanation has been proposed and tested once in a peer-reviewed setting, and that test did not confirm it.

Proposed explanation	What the evidence actually shows
Register: delve already fit formal and academic English	Matches the sharp rise measured in a 15.1 million abstract corpus of published academic writing
Reinforcement learning from human feedback rewards the word	Suggestive rather than confirmed. Preference-trained models show the pattern, but a direct human test rated delve-opening sentences more negatively, not more favorably
Human feedback workers speak an English variety where delve is more common	The one direct test of this, using the International Corpus of English, found no supporting evidence for delve or twenty other flagged words

Who Gets Mistaken for a Machine

The theory's accuracy matters less than what happened because people believed it anyway. Paul Graham's tweet, and the swift reply it drew, is the clearest illustration available. Thousands of Nigerian readers made the same point in different words: they had learned "delve" in school, read it in newspapers, and used it for decades, well before any chatbot existed. The writer Elnathan John's reply became the one people kept sharing, closing with the line that "no one used basic words like 'delve' in real life."

The same logic plausibly reaches South Asian English, which grew out of a similarly formal, Commonwealth-descended register and treats "delve" as just as unremarkable a word. That specific claim has not been tested as directly as the Nigerian case above, so it stays a reasonable extension here rather than a demonstrated fact. What is already documented, on this site and elsewhere, is that careful, correctly formal English written by someone who learned it outside the US or UK is exactly the profile a casual reader, and sometimes a detector, is most likely to flag. TextPulse's piece on why AI detectors are biased against non-native speakers covers that wider pattern in full.

AI Phrases, and the One Word That Replaces Each

Single words are half the problem. The other half is the stock phrase that takes four or five words to do the job of one, and a draft cleaned at word level can still run several hundred words long on these alone. Filler survives revision because it's grammatical, and it sounds careful. The hedged, nominalized phrase will read as more formal than the verb that was replaced; formality is what a first-year student is rewarded for, and what a second-language writer is trained to aim for. Both groups are doing nothing wrong. They are writing in the register they were handed.

The second reason is drafting speed. An opener like it should be noted that gives your hands something to type while your mind finishes the thought, and it survives to the final version because nothing about it is incorrect. The test is what deleting it would cost. If the sentence means the same without the phrase, the phrase was scaffolding.

Filler phrase	One-word replacement	What it was padding
due to the fact that	because	Cause
owing to the fact that	because	Cause
on the grounds that	because	Cause
the reason for this is that	because	Cause
in spite of the fact that	although	Concession
despite the fact that	although	Concession
in the event that	if	Condition
in the absence of	without	Condition
in order to	to	Purpose
for the purpose of	to	Purpose
with the aim of	to	Purpose
in an effort to	to	Purpose
so as to	to	Purpose
at this point in time	now	Time
at the present time	currently	Time
prior to	before	Time
subsequent to	after	Time
during the course of	during	Time
on a regular basis	regularly	Time
in the near future	soon	Time
a large number of	many	Quantity
a small number of	few	Quantity
the vast majority of	most	Quantity
a wide range of	many	Quantity
in the majority of cases	usually	Quantity
with regard to	about	Topic
with respect to	about	Topic
in relation to	about	Topic
in connection with	about	Topic
in the field of	in	Topic
within the context of	in	Topic
conduct an analysis of	analyse	Buried verb
perform an evaluation of	evaluate	Buried verb
carry out an investigation of	investigate	Buried verb
make an assessment of	assess	Buried verb
give consideration to	consider	Buried verb
reach a conclusion	conclude	Buried verb
have an effect on	affect	Buried verb
make use of	use	Buried verb
provide an explanation for	explain	Buried verb
is indicative of	indicates	Buried verb
is representative of	represents	Buried verb
has the ability to	can	Modality
is capable of	can	Modality
there is a possibility that	may	Modality
it is possible that	may	Modality
it should be noted that	delete	Hedge
it is worth noting that	delete	Hedge
it can be seen that	delete	Hedge
as previously mentioned	delete	Hedge

Two patterns account for most of that table. A buried verb hides an action inside a noun, so the sentence needs a weak verb to carry it, and both are billable words: an analysis gets conducted rather than something being analysed. A stock connective replaces a one-word logical join with a phrase saying the same thing more slowly. Learn those two shapes and you will catch phrases the table never listed.

The hedge group works differently, because the replacement is nothing at all. A sentence opening with it should be noted that has announced that a fact is coming instead of stating the fact. Delete the opener and the fact is still there, one clause earlier. TextPulse's free AI word cleaner flags this class of opener across a whole document.

When the Long Form Is the Correct Choice

A few of these phrases are doing real work, and cutting them on sight costs precision. The test is whether the long form carries information the short form drops.

Long form	Why it earns its place	Example
in order to	The bare infinitive can read as a complement rather than a purpose, and two infinitives in one clause need separating	Samples were diluted in order to reach the target concentration
prior to	Fixed terminology in clinical and regulatory writing, where the phrase names a defined time point	Consent was obtained prior to randomisation
with respect to	Mathematics and statistics: differentiation with respect to a variable is the standard name for the operation	The derivative with respect to time
it is possible that	Calibrated hedging: may attaches to the verb, while the long form hedges the whole proposition	It is possible that the effect reflects attrition rather than treatment
in the absence of	Methods writing where a condition is defined by what was withheld	Growth was measured in the absence of the inhibitor

Everything in this second table also appears in the first one. That's the point. The default, you get it from a lookup table. And by the way, defaults are right most of the time. Keep the default, move on. Where the long form names a defined time point, a mathematical operation, or a hedge covering the whole proposition, keep it and move on.

How the List Keeps Changing

The list isn't fixed. Wikipedia's own editing guideline for spotting machine-written additions, maintained by volunteers who revert this kind of edit daily, tracks the fashion moving in real time. Its 2023-to-mid-2024 wave centers on words such as "boasts", "crucial", "pivotal" and "tapestry". By the middle of 2025 the same guideline was tracking a different cluster, including "align with", "fostering" and "showcasing", with plainer words such as "enhance" and "highlighting" carrying more of the load than the showier ones had done a year earlier. None of this is centrally coordinated.

A list frozen at one moment goes stale fast for exactly this reason. What holds up is the underlying habit: formal words, reached for slightly too often, regardless of which specific ones are fashionable this year.

How to Remove AI Words From a Finished Draft

The vocabulary pass belongs near the end of revision, after the argument is settled and before the final proofread. Replacing a verb changes what follows it. Swap a noun phrase for a verb and the preposition after it changes too, subject-verb agreement can break, and a hedge can quietly gain or lose strength. Every one of those is a proofreading problem, so the proofread has to come after this pass rather than before it.

The earlier trap is more expensive. A vocabulary pass run on a draft whose discussion is still moving means editing sentences that will be deleted, and every deleted sentence takes its edit with it. Settle the structure, then the paragraphs, then the words. The full sequence for a long document sits in the guide to proofreading a thesis; the table below is the vocabulary part of it.

Pass	What you scan for	What you change	Why it sits here
1. Argument	Sections that will move or be cut	Structure only, no sentences	Any word edited inside a paragraph you later delete is wasted
2. Density	Paragraphs carrying three or more flagged words	Nothing yet, only marking	Finding the clusters first stops you editing paragraphs that were already clean
3. Replacement	One marked paragraph at a time	The verb first, then the nouns around it	The verb decides what the rest of the clause has to be
4. Restoration	Words your field uses as technical vocabulary	Put back the ones a swap took by accident	A blanket search-and-replace cannot tell a term of art from a habit
5. Proofread	Agreement, tense, prepositions, hedge strength	Whatever pass three broke	Replacement creates exactly these errors, so it cannot be checked earlier

Where the Flagged Words Sit in a Research Paper

Flagged vocabulary is not spread evenly through a manuscript. It gathers at the joints: the first and last sentence of the abstract, the opening paragraph of the discussion, the transition at the head of each literature review paragraph, and the limitations section. Those are the places a writer reaches for a ready-made phrase, because they are the places where the next sentence is hardest to start.

Methods sections and figure captions tend to be the cleanest part of a paper, because they describe operations that already have specific verbs attached. A flagged word in a caption arrived by habit rather than by need, which makes it worth a second look.

Search by stem rather than by word. Most of these words carry four or five inflections, and searching for the full word finds one of them.

• Search the stem: delv catches delve, delves, delved and delving in one pass, and underscor catches underscore, underscores and underscoring.
• Search both spellings when a manuscript mixes conventions, because utilize and utilise are two separate hits and your find box will not treat them as one.
• Search the participle on its own. Leveraging survives a pass that only searched for leverage, and showcasing and highlighting do the same.
• Use your word processor's find-all count instead of clicking through the hits. The number is the finding: two instances of a formal verb across a thesis chapter is ordinary, fourteen is a pattern.
• Run the whole list over the abstract again at the very end. It is the part reviewers read first and the part most often rewritten last.

Run the marked paragraphs through the free AI word cleaner if you would rather have the density count done for you. The tool marks; the decision about each word is yours, and the next two parts are how to make it.

What to Replace the Word With in Academic Register

A replacement is a rebuild of the clause, and the thesaurus swap is the failure mode. Exchanging one formal word for another formal word keeps the shape of the sentence and keeps its emptiness. Four moves cover almost every case in an academic manuscript.

First, restore the verb that names what actually happened. A results sentence based on a display verb obscures the action. Something showcased a relationship; but in the paper, we know if the variable correlated, predicted, or increased. Use the specific verb. It's shorter, and it will survive peer review better. Peer reviewers can look at your data to see if you got it right.

Second, replace an abstract noun with the measurement. A sentence offering insights into a mechanism can name them: the effect size, the two conditions where it held, the one where it collapsed. Academic readers are not asking for less formality here. They are asking for the number.

Third, delete the intensifier instead of swapping it. Meticulously validated is validated. Rigorously tested is tested. The adverb carried no information in either sentence, and trading it for a milder adverb keeps the padding and only makes it quieter.

Fourth, match the hedge strength on purpose. The choice of strength is what makes or breaks a discussion section. You can't have an automated tool do this for you. You may be tempted to use a strong verb like "assert" when really the data only support a weaker one like "suggest." A finding that emphasizes a mechanism says more than a finding consistent with one. Use the strength you can back up under review.

One Sentence, Rebuilt

Take a discussion opener carrying four flagged words at once: "This comprehensive analysis underscores the multifaceted mechanisms that facilitate improved outcomes." Every word is real English and the clause is grammatical, which is why it slips past a self-edit. The rebuild names the operation instead: "The analysis identified two mechanisms that improved outcomes and one that did nothing." No content was lost. What went was the padding, and about a third of the words.

When the Flagged Word Is the Right Word

Some of these words are load-bearing in a specific discipline, and a blanket replacement damages the paper. A statistician writing about robust standard errors is naming a defined class of estimator. A systematic reviewer describing a comprehensive search is naming a protocol requirement, and cutting it weakens a methods claim that had to be made in that form.

The test is whether the word points at something checkable. Where it names a procedure, a statistic, or a defined construct in your field, it stays. Where it is doing tone work, it goes.

Word	Where it is a term of art	Cut it when
robust	Robust standard errors, robust regression, a robustness check	It describes how convincing your own argument feels
significant	A stated alpha level, with the test and the p-value reported	It is standing in for large, important or noticeable
comprehensive	A comprehensive search strategy named in a review protocol	It praises the thoroughness of your own analysis
critical	Critical value, critical care, critical theory, critical path	It is a synonym for important
novel	The contribution statement a journal asks you to make once	It has already appeared earlier in the same paper
elucidate	Structure elucidation in chemistry and structural biology	It is standing in for explain or describe
underpinning	A named theoretical framework the study is built on	It is standing in for based on

How to Run a Phrase Swap Without Breaking the Sentence

Replacing a buried verb moves things around. The noun that was the object of the weak verb becomes the object of the real one, the article in front of it goes, and any adjective modifying that noun becomes an adverb or is dropped. A detailed analysis was conducted of the transcripts becomes the transcripts were analysed in detail.

Three failures are worth watching for afterwards. Tense drifts when you rebuild a passive clause. Agreement breaks when a singular noun turns into a plural verb, or the reverse. Hedge strength shifts when a long modal phrase collapses into a single modal, which matters in a discussion section, because may and can do different jobs in a claim about your results.

Read the changed sentence aloud once. If it runs short and flat, join it to the sentence after it and leave the padding out. A sentence shortener does the mechanical part over a long paragraph, and the judgement about which pairs belong together stays with you.

What This Does to Your Word Count

The arithmetic decides whether this is a style exercise or a submission fix. Each swap in the cause and purpose groups saves three or four words. The buried verb group saves two or three. The hedge group saves five or six, because the whole opener goes. Fifty swaps at an average of three words is a hundred and fifty words back.

For a chapter over its limit that is a real recovery, and it is the cheapest cut available, because no content left the page. Every other route to a word limit involves deciding what to lose, and the guide to reducing word count in a thesis works through those in order of how much they hurt.

How to Check the Manuscript After the Pass

Read the changed paragraphs aloud, with the tracked changes hidden. A replacement that broke a preposition or dropped an article is audible in a way it stays invisible on screen. Then re-run the density count over the whole document and compare it against the count from pass two. A pass that took fourteen instances down to eleven did not really happen.

A cleaner vocabulary does not entitle anyone to claim a detector has been satisfied, and this site will not make that claim for a word list or for its own AI humanizer, which reports an estimated Human Score computed from the text rather than a verdict from any detector. What the pass buys is a manuscript that reads as though a person chose each verb.

Do These Words Prove AI Wrote It?

No. A careful writer under deadline pressure, a non-native English speaker reaching for safe formal vocabulary, and a student who was taught these exact words in a writing class can all produce a paragraph that scores high against this list without a chatbot anywhere nearby. Treating the table above as proof of misconduct is the mistake that gets careful writers falsely accused, and it is worth naming as a mistake rather than repeating it quietly. TextPulse's own products exist because the reverse problem is just as real: a genuine piece of human writing, unlucky enough to cluster a few of these words in one paragraph, can get pulled into a review it never deserved.

Statistical detectors work on a related but different signal entirely, covered in TextPulse's piece on how AI detectors work, and TextPulse's own AI humanizer reports an estimated Human Score computed from the text in front of it rather than a verdict from any detector. A word list like this one is a starting point for a human decision, not a replacement for one. The rest of TextPulse's free tools collection covers the other levels, rhythm, punctuation and structure, for anyone checking a full draft rather than one paragraph.

Punctuation carries the same signature, and the em dash habit is examined on its own page. Two labels get used for prose in this state, and what AI slop means is set out separately.

The next time a paragraph feels slightly off, do not reach for a single synonym and call it fixed. Scan for density instead: two or three of these words doing real work is ordinary writing, four or five doing no work at all is the tell, and the difference is something any reader can learn to see without running anything through a detector first.