As the article points out at the end, these aren't bad per se. The issue is that LLMs overuse them, and we're all getting the same(-ish) LLM. It's not so different from how people sometimes have their idiosyncratic phrasings they use all the time.
I work on research studying LLM writing styles, so I am going to have to steal this. I've seen plenty of lists of LLM style features, but this is the first one I noticed that mentions "tapestry", which we found is GPT-4o's second-most-overused word (after "camaraderie", for some reason).[1] We used a set of grammatical features in our initial style comparisons (like present participles, which GPT-4o loved so much that they were a pretty accurate classifier on their own), but it shouldn't be too hard to pattern-match some of these other features and quantify them.
If anyone who works on LLMs is reading, a question: When we've tried base models (no instruction tuning/RLHF, just text completion), they show far fewer stylistic anomalies like this. So it's not that the training data is weird. It's something in instruction-tuning that's doing it. Do you ask the human raters to evaluate style? Is there a rubric? Why is the instruction tuning pushing such a noticeable style shift?
Another one that seems impossible for LLMs to avoid: breaking article into a title and a subtitle, separated by a colon. Even if you explicitly tell it not to, it'll do it.
No mention of Claude/ChatGPT's favourite new word genuine and friends? They also like using real and honest when giving advice. Far as I can tell this is a new-ish change.
> Honestly? We should address X first. It's a genuine issue and we've found a real bug here.
Honorable mention: "no <thing you told me not to do>". I guess this helps reassure adherence to the prompt? I see that one all the time in vibe coded PRs.
The article has been slashdotted so I don't know if this one is in there but:
One I've seen Gemini using a lot is the "I'll shoot straight with you" preamble (or similar phrasing), when it's about to tell me it can't answer the question.
Weirdly, LLMs seem to break with these instructions. They simply ignore them, almost as if the pretraining/RL weights are so heavy, no amount of system prompting can override it
It's a beauty. We can easily detect the issues with Youtubers that generate scripts from this tool. I've noticed these tropes, after 30 seconds, remove, block, and do not recommend any further. I hope to train the algorithm to detect AI scripts and stop recommending me those videos. It's honestly turned me off from YouTube so much, or I find myself going to my "subscribed" tab and going to content creators that still believe in the craft.
I’ve taken it one step further. YouTube as a front end is awful, and I’ve had enough. Tons of little dark patterns made to keep you on the site, annoying algorithms taking you places you never want to go, shitty ai slop, the whole nine yards. But I still like certain channels. As a result I’m doing everything self hosted now - not just YouTube but literally every single piece of digital media I consume. For YouTube I had to create a rotating pool of 5 residential ISP proxies - replaced as soon as YouTube download bot restrictions kick in - and rotated weekly either way.
With this I am able to get all my favorite subs onto my actual hard drive, with some extra awesome features as a result: I vibe coded a little helper app that lets me query the transcript of the video and ask questions about what they say, using cheap haiku queries. I can also get my subs onto my jellyfin server and be able to view it in there on any device. Even comments get downloaded.
All these streamers have gone too far trying to maximize engagement and have broken the social contract, so I see this as totally fair game.
IIRC, it's well documented that negative instructions tend to be ineffective - possibly through some sort of LLM analogue to the "pink elephant paradox", or simply because the language models are unable to recognize clichés in their own output.
"The "It's not X -- it's Y" pattern, often with an em dash. The single most commonly identified AI writing tell. Man I f*cking hate it. AI uses this to create false profundity by framing everything as a surprising reframe. One in a piece can be effective; ten in a blog post is a genuine insult to the reader. Before LLMs, people simply did not write like this at scale."
This one hit home... the first time I ever saw Claude do it I really liked it. It's amazing how quickly it became the #1 most aggravating thing it does just through sheer overuse. And of course now it's rampant in writing everywhere.
If you sound like a car ad from Road & Track, I'm going to flag you as bot.
"No rough handling. No struggles to accelerate. Just pure performance. The new Toyota GT. It's not just a car—it's a revolution."
Most of the tropes listed on this page give text a more "car ad" (or sometimes "movie trailer") quality. I wonder if magazine scans and press releases unduly weighted the training set.
If you participate in certain online communities where posts used to generally share real ideas and ask real beginner questions, you get tired of it. I am especially tired of seeing "it's not X - it's Y" on /r/MachineLearning posts, claiming that they've found some "geometry" or basic PyTorch code which they think will solve AI hallucinations. And it's becoming clear these people are not just doing this sort of a thing on a whim, but spending days in delusional conversations with the AI.
This makes me think of the attractiveness of overly bad writing to writers, as a challenge, the most obvious example being the bulwer-lytton award, or the instinctive ignoring of instructions from fiction magazines that might say "we don't want any stories about murderous grandparents, French bashing, bestiality, bank robbers from the future, or kind-hearted Nazis - and especially do not try to be super brilliant and funny and send us your story about kind-hearted Nazi bank-robbing french-bashing grandparents that like killing people and having sexy fun times with barnyard animals! Because every original thinker like you thinks they are the first to have come up with that idea!" and then as a writer you feel challenged to do exactly what they say they don't want because what a glorious triumph if you manage to outdo everyone and get your dreck published because it's dreck that is so bad it's good!
It does not seem like there are lots of people who are perversely inclined to write a story with all these tropes and words in it, but surely there must be some, because if you make something that beats the LLM (by being creatively good) using all the crap the LLM uses, it would seem some sort of John Henry triumph (discounting the final end of John Henry of course, which is a real downer)
Don't forget "The Ludlum Delusion"- every header in an article or readme reads like a Robert Ludlum novel title, ie "The [Noun:0.9|Adjective:0.1] [Noun]".
Negative parallelism is a staple of briefs. "This case is not about free speech. It is about fraud." It does real work when you're contesting the other side's framing.
Tricolons and anaphora are used as persuasion techniques for closing arguments and appellate briefs.
Short punchy fragments help in persuasive briefs where judges are skimming. "The statute is unambiguous."
As with the em dash - let's not throw the baby out with the bath water.
You know how no one ever wrote their own software and then generative AI came along and suddenly we could have app meals home-cooked by barefoot developers? (The use of such cottagecore terminology for a process that requires being an ongoing client of a hundred-gigabuck, planet-burning megacorporation rubs me in many wrong ways.)
If AI finally gets rid of the thing that drove me nuts for years: "leverage" as a verb mean roughly "to use"—when no human intervention seems to work, then I shall be over-the-moon happy. I once worked at a place where this particular word was lever—er, used all the damn time and I'd never encountered something so NPC-ish. I felt like I was on The Twilight Zone. I could've told you way back then that you sounded like a bot doing that, now people might actually believe me and thank god.
I will stick by the em dashes however. And I might just start using arrows too. Compose - > → right arrow. Not even difficult.
As the article points out at the end, these aren't bad per se. The issue is that LLMs overuse them, and we're all getting the same(-ish) LLM. It's not so different from how people sometimes have their idiosyncratic phrasings they use all the time.
https://web.archive.org/web/20260307224900/https://tropes.fy...
I work on research studying LLM writing styles, so I am going to have to steal this. I've seen plenty of lists of LLM style features, but this is the first one I noticed that mentions "tapestry", which we found is GPT-4o's second-most-overused word (after "camaraderie", for some reason).[1] We used a set of grammatical features in our initial style comparisons (like present participles, which GPT-4o loved so much that they were a pretty accurate classifier on their own), but it shouldn't be too hard to pattern-match some of these other features and quantify them.
If anyone who works on LLMs is reading, a question: When we've tried base models (no instruction tuning/RLHF, just text completion), they show far fewer stylistic anomalies like this. So it's not that the training data is weird. It's something in instruction-tuning that's doing it. Do you ask the human raters to evaluate style? Is there a rubric? Why is the instruction tuning pushing such a noticeable style shift?
[1] https://www.pnas.org/doi/10.1073/pnas.2422455122, preprint at https://arxiv.org/abs/2410.16107. Working on extending this to more recent models and other grammatical features now
You may be interested in my collection of links about AI's writing style: https://dbohdan.com/ai-writing-style. I've just added your preprint and tropes.fyi. It has "hydrogen jukeboxes: on the crammed poetics of 'creative writing' LLMs" by nostalgebraist (https://www.tumblr.com/nostalgebraist/778041178124926976/hyd...), which features an example with "tapestry".
> Why is the instruction tuning pushing such a noticeable style shift?
Gwern Branwen has been covering this, including methods to break out: https://gwern.net/doc/reinforcement-learning/preference-lear....
The RLHF is what creates these anomalies. See delve from kenya and nigeria.
Interestingly, because perplexity is the optimization objective, the pretrained models should reflect the least surprising outputs of all.
Wikipedia also has an exhaustive guide, though it's not fun finding tropes you use yourself (I'm very guilty of the false range "from X to Y" thing):
https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
Another one that seems impossible for LLMs to avoid: breaking article into a title and a subtitle, separated by a colon. Even if you explicitly tell it not to, it'll do it.
If this bugs you, open chatGPT personality settings, choose “efficient” base style, and turn off the enthusiasm and warmth sliders
It makes a tremendous difference. Almost everything on this list is the emotional fluff ChatGPT injects to simulate a personality.
No mention of Claude/ChatGPT's favourite new word genuine and friends? They also like using real and honest when giving advice. Far as I can tell this is a new-ish change.
> Honestly? We should address X first. It's a genuine issue and we've found a real bug here.
Honorable mention: "no <thing you told me not to do>". I guess this helps reassure adherence to the prompt? I see that one all the time in vibe coded PRs.
Similarly, "X that actually works"
A substantive human-written resource: https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
I sort of think the whole middlebrow angst thread about Bourdieu going on right now applies to LLM writing
https://news.ycombinator.com/item?id=47260028
The article has been slashdotted so I don't know if this one is in there but:
One I've seen Gemini using a lot is the "I'll shoot straight with you" preamble (or similar phrasing), when it's about to tell me it can't answer the question.
What we really need is a browser plugin underlining these patterns, especially for comments.
Weirdly, LLMs seem to break with these instructions. They simply ignore them, almost as if the pretraining/RL weights are so heavy, no amount of system prompting can override it
It's a beauty. We can easily detect the issues with Youtubers that generate scripts from this tool. I've noticed these tropes, after 30 seconds, remove, block, and do not recommend any further. I hope to train the algorithm to detect AI scripts and stop recommending me those videos. It's honestly turned me off from YouTube so much, or I find myself going to my "subscribed" tab and going to content creators that still believe in the craft.
I’ve taken it one step further. YouTube as a front end is awful, and I’ve had enough. Tons of little dark patterns made to keep you on the site, annoying algorithms taking you places you never want to go, shitty ai slop, the whole nine yards. But I still like certain channels. As a result I’m doing everything self hosted now - not just YouTube but literally every single piece of digital media I consume. For YouTube I had to create a rotating pool of 5 residential ISP proxies - replaced as soon as YouTube download bot restrictions kick in - and rotated weekly either way.
With this I am able to get all my favorite subs onto my actual hard drive, with some extra awesome features as a result: I vibe coded a little helper app that lets me query the transcript of the video and ask questions about what they say, using cheap haiku queries. I can also get my subs onto my jellyfin server and be able to view it in there on any device. Even comments get downloaded.
All these streamers have gone too far trying to maximize engagement and have broken the social contract, so I see this as totally fair game.
IIRC, it's well documented that negative instructions tend to be ineffective - possibly through some sort of LLM analogue to the "pink elephant paradox", or simply because the language models are unable to recognize clichés in their own output.
"The "It's not X -- it's Y" pattern, often with an em dash. The single most commonly identified AI writing tell. Man I f*cking hate it. AI uses this to create false profundity by framing everything as a surprising reframe. One in a piece can be effective; ten in a blog post is a genuine insult to the reader. Before LLMs, people simply did not write like this at scale."
This one hit home... the first time I ever saw Claude do it I really liked it. It's amazing how quickly it became the #1 most aggravating thing it does just through sheer overuse. And of course now it's rampant in writing everywhere.
If you sound like a car ad from Road & Track, I'm going to flag you as bot.
"No rough handling. No struggles to accelerate. Just pure performance. The new Toyota GT. It's not just a car—it's a revolution."
Most of the tropes listed on this page give text a more "car ad" (or sometimes "movie trailer") quality. I wonder if magazine scans and press releases unduly weighted the training set.
I think it's more likely that car ads and chatbots are both optimizing for the same thing i.e. grabbing the audience's attention.
Weird to care about a harmless construction along with punctuation.
If you participate in certain online communities where posts used to generally share real ideas and ask real beginner questions, you get tired of it. I am especially tired of seeing "it's not X - it's Y" on /r/MachineLearning posts, claiming that they've found some "geometry" or basic PyTorch code which they think will solve AI hallucinations. And it's becoming clear these people are not just doing this sort of a thing on a whim, but spending days in delusional conversations with the AI.
weirder still to immerse your brain in sewage and take pride in your lack of discernment.
This makes me think of the attractiveness of overly bad writing to writers, as a challenge, the most obvious example being the bulwer-lytton award, or the instinctive ignoring of instructions from fiction magazines that might say "we don't want any stories about murderous grandparents, French bashing, bestiality, bank robbers from the future, or kind-hearted Nazis - and especially do not try to be super brilliant and funny and send us your story about kind-hearted Nazi bank-robbing french-bashing grandparents that like killing people and having sexy fun times with barnyard animals! Because every original thinker like you thinks they are the first to have come up with that idea!" and then as a writer you feel challenged to do exactly what they say they don't want because what a glorious triumph if you manage to outdo everyone and get your dreck published because it's dreck that is so bad it's good!
It does not seem like there are lots of people who are perversely inclined to write a story with all these tropes and words in it, but surely there must be some, because if you make something that beats the LLM (by being creatively good) using all the crap the LLM uses, it would seem some sort of John Henry triumph (discounting the final end of John Henry of course, which is a real downer)
If only we could fix how it writes like garbage
Another trope: longer README.md's than anyone would make, or want.
Yes, to me this is a huge tell. Especially when it goes into detail about pros and cons (using a table) on the most superficial points.
Don't forget "The Ludlum Delusion"- every header in an article or readme reads like a Robert Ludlum novel title, ie "The [Noun:0.9|Adjective:0.1] [Noun]".
This list reads like, "AIs are not your typical braindead person on the street. They actually use a decent but not crazily advanced vocabulary."
I mean, "tapestry" is a great word for something that is interconnected. Why not use it?
Many of these are standard fare in legal writing.
Negative parallelism is a staple of briefs. "This case is not about free speech. It is about fraud." It does real work when you're contesting the other side's framing.
Tricolons and anaphora are used as persuasion techniques for closing arguments and appellate briefs.
Short punchy fragments help in persuasive briefs where judges are skimming. "The statute is unambiguous."
As with the em dash - let's not throw the baby out with the bath water.
> (let's play cat and mouse!).
No thanks, I hate this large scale social experiment
You know how no one ever wrote their own software and then generative AI came along and suddenly we could have app meals home-cooked by barefoot developers? (The use of such cottagecore terminology for a process that requires being an ongoing client of a hundred-gigabuck, planet-burning megacorporation rubs me in many wrong ways.)
If AI finally gets rid of the thing that drove me nuts for years: "leverage" as a verb mean roughly "to use"—when no human intervention seems to work, then I shall be over-the-moon happy. I once worked at a place where this particular word was lever—er, used all the damn time and I'd never encountered something so NPC-ish. I felt like I was on The Twilight Zone. I could've told you way back then that you sounded like a bot doing that, now people might actually believe me and thank god.
I will stick by the em dashes however. And I might just start using arrows too. Compose - > → right arrow. Not even difficult.
This kills the headline baiting tech blogger.