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AI words to avoid: the words ChatGPT overuses, charted

These are the AI words to avoid if you want your writing to sound like your own: words chatbots reach for far more often than people do. In 2024, "delves" turned up in 357 of every 100,000 PubMed abstracts, up from 7.5 in 2022. One word alone proves nothing.

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The scan runs in memory on Numen's server and the passage is discarded with the response.

The words, measured

WordForms on the list2010-202420222024Ratio
delvedelves, delved, delving, delve7.535747.8×
showcaseshowcasing, showcases, showcased1722913.8×
meticulouslymeticulously1616610.5×
surpasssurpassing, surpasses332778.4×
commendablecommendable5.9488.3×
excelexcels4.5357.7×
underscoreunderscored341965.8×
intricatelyintricately17985.7×
comprehendcomprehending221235.7×
groundbreakinggroundbreaking14825.7×
renownedrenowned221074.9×
grapplegrappling5.2264.9×
bolsterbolstering12544.6×
revolutionizerevolutionizing, revolutionize18734.2×
alignaligns391554.0×
formidableformidable391513.9×
consolidateconsolidates8.8343.9×
emphasiseemphasising22853.8×
dependabledependable13483.8×
unveilunveils321213.8×
discerniblediscernible331223.7×
expediteexpediting11403.6×
overlookoverlooking14503.5×
pinpointpinpointed, pinpointing19623.4×
scrutinizescrutinizing7.2233.2×
seamlesslyseamlessly24763.2×
advocateadvocating381203.2×
craftcrafting11353.2×
adeptadept9.1283.1×
garnergarnering8.3263.1×
harnessharnesses11343.0×

Shares per 100,000 abstracts. Each sparkline runs from 2010 to 2024 and starts at zero on its own scale, so its 2022 point sits at 1/ratio of its height.

Famous AI words we leave alone

Every one of these rose after ChatGPT arrived. The checker leaves them alone for the reason in each row.

Word2010-202420222024RatioWhy it is not on the list
underscores1041,43913.8×Too common before ChatGPT. "underscored" is on the list.
intricate1349927.4×Too common before ChatGPT. "intricately" is on the list.
tapestry1.05.65.5×Too rare in abstracts to call.
realm452265.0×A noun, and nouns follow the topic.
nuanced952983.1×Too common before ChatGPT.
meticulous551723.1×Too common before ChatGPT. "meticulously" is on the list.
pivotal5651,7303.1×Too common before ChatGPT.
multifaceted1985722.9×Rose less than the listed words.
crucial2,9627,0602.4×Rose less than the listed words.
Every year, every word form
Form201020112012201320142015201620172018201920202021202220232024
delves1.21.71.50.52.01.92.01.62.63.84.24.97.575357
delved0.51.30.80.60.91.41.81.71.52.22.82.83.81771
delving1.20.71.81.11.31.62.62.91.62.92.64.05.31855
delve3.33.84.54.94.75.34.57.38.01112182065196
showcasing1.22.61.51.93.03.95.55.77.28.612141752229
showcases2.64.62.85.16.36.45.69.6131317202345109
showcased3.33.03.03.24.66.06.58.810131420224395
meticulously9.28.79.49.39.99.81114141414161650166
surpassing1212111412161521242328303386277
surpasses6.811129.110101213141718202242102
commendable1.92.22.52.93.22.73.73.84.44.55.15.25.91548
excels1.61.92.01.63.01.72.12.92.43.63.43.84.51035
underscored2426222528262730262728313454196
intricately131114131313121414131416173198
comprehending7.87.69.09.27.88.28.910111312162248123
groundbreaking8.29.29.58.79.912111111111213142782
renowned1214161417192020201720232238107
grappling2.23.63.02.22.82.23.33.83.55.05.75.65.21126
bolstering5.15.64.94.95.46.24.88.77.98.69.311122054
revolutionizing8.28.59.49.11112141414151617183173
revolutionize2220222325232927303034363267124
aligns1314151614191824282933323963155
formidable3635343136333432343836403961151
consolidates4.85.15.45.47.26.97.36.27.38.37.99.38.81334
emphasising161718191718191917202023223085
dependable128.88.1129.09.19.59.3109.98.911132948
unveils7.96.9118.513131517202426263247121
discernible4033414647394137393434303356122
expediting4.85.56.36.27.97.59.58.29.0111314112040
overlooking7.15.57.18.27.78.8101112121316142250
pinpointed11119.5101110121213131818192362
pinpointing107.410118.410131212111313132042
scrutinizing5.14.03.24.04.85.64.75.65.55.57.57.27.21223
seamlessly111113151716171720212224243476
advocating2222232626252728273332373848120
crafting4.23.04.22.73.74.14.76.57.47.78.611111535
adept9.0129.89.19.99.011109.512119.99.11228
garnering2.32.93.32.64.05.24.54.25.35.87.76.48.31126
harnesses3.92.95.15.65.15.68.57.57.69.08.311112034
underscores90868498969193959898951041042921,439
intricate889510193103109117117118113128137134326992
tapestry0.30.70.70.70.80.31.10.50.71.21.41.21.02.45.6
realm4235414139444844514648484593226
nuanced21212429374746556267748895125298
meticulous5451565051515356555156605579172
pivotal4404454514474544704935065165235495675657081,730
multifaceted103112116119132138137148161166179197198266572
crucial1,6561,7201,7641,8511,9262,0062,0952,2132,2872,4262,6122,7692,9624,0197,060

Why models overuse these words

Nobody knows for sure, though two explanations have real evidence behind them. The first comes from Tom Juzek and Zina Ward, who presented a paper at COLING 2025 asking why words like "delve", "intricate" and "underscore" had started turning up so often in scientific abstracts. They tested the obvious suspects first: the design of the models, the training algorithms and the text the models learned from. They found no evidence for any of them. What did fit was the last stage of training, reinforcement learning from human feedback, where people rate a model's answers and the model learns to produce more of what they rated highly. Even that came with a wrinkle, because in their online study people seemed to respond to "delve" differently from the other words. We'd call that a strong lead rather than a closed case.

The second explanation is about who those raters were. Writing in the Guardian in April 2024, Alex Hern pointed out that this feedback takes hundreds of thousands of hours of work, which is why AI companies send it to English-speaking workers in the global south, where the workers are cheap to hire. He also noted that "delve" is much more common in Nigerian business English than in British or American English. If the people rating and writing the answers used the word, the model may simply have picked it up from them. The two explanations don't compete; the second may describe how the first played out. The full story of "delve" goes through both.

What a list of AI words to avoid can't tell you

We build an AI detector, so we have every reason to want a list like this to be decisive. It isn't. Every word on it is ordinary English, and the charts, which run back to 2010, show people using each one years before any chatbot was writing abstracts. The difference now is frequency. A single hit in your paragraph tells you almost nothing about who wrote it, and three or four of them only tell you the paragraph is worth a slower read.

Getting this wrong has a cost: "delve" is everyday vocabulary in Nigerian English, so flagging it on sight flags Nigerian writers who have never opened a chatbot.

That's why the checker on this page only reports what it finds. It marks the listed words in your text and leaves the judgment to you. It never tells you who wrote something. If you're checking your own draft, a marked word is a chance to ask whether it's the word you meant. If you're checking someone else's, treat it as a reason to talk to them, never as the answer.

Vocabulary is one tell among many. Signs of AI writing covers the sentence habits models fall into, each with examples.

Check a passage in the free online detector

Share of PubMed abstracts containing the word, per year, 2010-2024. Ratio: 2024 share divided by 2022 share.