I find this repellent; why not, instead of trying to push unwelcome generated prose below the radar, stop trying to waste everyone's time? People don't object to these patterns because they hate lists of three; they object to them in this context because of what they signal about the content.
If using AI to write is nothing to be ashamed of, then you shouldn't feel the need to hide it. If it is something to be ashamed of, then you should stop doing it. If someone objects to you poisoning a well, the correct response is not to use a more subtle poison.
I sometimes like having my content editorialized. Some of the LLM writing tropes are ok to me—I'd delete them if I added this prompt to my instructions (but I wouldn't). But my editorial preferences—the sense of voice and tone I want the LLM to make—are rarely these tropes. Instead, I have a positive prompt of the angles I do enjoy.
However, what is cloying about these tropes for many is that they're becoming empty words. Instead of tack-sharp summaries or reductions to simple understanding, the model is spilling extra tokens for minimal value—I don't need to read "it's not X, it's Y" for the n-th time today. I'd really prefer tighter, more succinct reading that actually directly quotes sources (which modern models rarely do to avoid copyright traps).
I sometimes use AI to quickly summarise a handful of several MB long PDF files.
This allows me to order them in order of the relevance to start getting my data and information faster.
Applying a constraints like in the published template will make it slightly less awful. It's going to be discarded anyway, but at least the experience is going to be better.
Not every LLM output is going to be published for you to consume. If hazard a guess most never sees the light of the day.
Treating the act of refining text as a confession of shame misses the point of how writing works. Whether a draft begins as a model output, a dictation, or a scribbled note, the final responsibility belongs to the person who hits publish.
Improving prose to remove predictable patterns is the work of an editor. This process ensures the content is worth reading and respects the audience's time.
Comparing a software tool to "poisoning a well" turns a debate over style into a moral crisis that doesn’t fit the situation. If the information is accurate and the writing is clear, the water in the well is fine, regardless of the pump used to get it there. If the water tastes good, complaining about the plumbing is just a distraction.
Parents complaint is explicitly not about the style of the prose, use whatever you want to check your grammar and reduce redundancy. The complaint of poisoning the well is regarding content that is not intended to express anything at all, the old “why would I read what nobody bothered to write”
The issue is that you're conflating the process of transcription with the act of expression. If I feed an LLM my own raw research notes and technical observations and use it to help structure those thoughts into a readable essay, I haven't "avoided writing".
The "why would I read what nobody bothered to write" argument only applies to people who ask a bot to hallucinate an opinion from scratch. It doesn't apply to authors using the tool to clarify their own ideas.
LLM-generated text that is a hallucinated-from-scratch opinion is practically indistinguishable from LLM-generated text that is rooted in your research notes.
I find putting the former into my brain abhorrent to such an extent that I am willing to forego reading the few instances of the latter. I'd much rather have your raw research notes and observations.
> If I feed an LLM my own raw research notes and technical observations and use it to help structure those thoughts into a readable essay, I haven't "avoided writing".
> The "why would I read what nobody bothered to write" argument only applies to people who ask a bot to hallucinate an opinion from scratch. It doesn't apply to authors using the tool to clarify their own ideas.
You're wasting my time if you share LLM writing. If you're going to do it that way, share your notes and your prompt. Otherwise, you're being inconsiderate.
>Comparing a software tool to "poisoning a well" turns a debate over style into a moral crisis that doesn’t fit the situation. If the information is accurate and the writing is clear, the water in the well is fine, regardless of the pump used to get it there. If the water tastes good, complaining about the plumbing is just a distraction.
Speaking of poisoning wells, have you heard of this thing called Search Engine Optimization? Absolutely ruined the Internet.
For example it ignores the gazillion medium(-like) "articles" that are not much more than the output of a prompt. Here AI is not about style, is about content too. If you open such a post, maybe with the intent of learning anything, and you realize is AI slop, you might close it. Making it harder to recognize is poisoning the well in such cases.
If you are serious about sharing written ideas, I suggest you avoid using this type of prompts at all cost. I've worked with LLMs to write on my blog and they are pretty good at first glance [0]. But do it a few time and you'll notice that those tropes are the least of your problems. Not only all your articles will sound the same, but you'll see that same voice on other blogs, news articles, white paper, etc. It's as if they were all written by Mo Samuels. Readers are often here for the author's voice, not just the content of the text.
I often hear this here: "if you don't bother writing, why should I bother reading?" In fact, save us some time and just share the prompt.
> I often hear this here: "if you don't bother writing, why should I bother reading?"
That is an opinion somebody shared on X which has been mindlessly repeated over and over again in other places such as this site.
Why do you value those comments when all they are doing is parroting something they didn’t think themselves? It seems to undermine your point entirely. There is zero originality or effort in those comments. Why are you bothering to read them?
Copying and pasting somebody else’s opinion from one social media site to another is no more virtuous than what you are complaining about.
I value that opinion because it resonates with me. When I use an LLM to write an article, it's usually because I don't have the time or energy to go through my normal process of writing.
Sure, I still end up with a polished article, but a lot of it is not entirely my idea or something I would have written through the filter of my own experience. So in order to share my true take on a subject, I have to go through the struggle of writing and bouncing of ideas in my head, which almost always results in a better output.
I have seen people suggest that the problem is that LLMs let you express any of your ideas, but the number of people with ideas worth expressing is limited.
In a sense I think this is accurate, but not inevitable. I think there is a lack of creative thinking, but it has come from a world that doesn't value it and suppresses difference.
There is a brilliant line in Treehouse of Horrors IV where Principle Skinner says "Now I've gotten word that a child is using his imagination, and I've come to put a stop to it." Which is just the perfect comment on the modern education system.
Models trained on the lack of diversity will push one way, but I think it will also avenues for expression that didnb't exist before. The balance will come from how we react and support what we would like to have happen
I think it has more to do with LLM's being statistical models than human creativity lacking in the input. The creativity and millions of voices and tones may be there, but since these models tend to go for the most likely next words, polishing this away becomes a feature.
A text by a human mind may be seen as a jagged crystal with rough edges and character. Maybe not perfectly written but it's special.
An LLM takes a million of crystals and trims the most likely tokens to be chosen into what would rather appear as a smooth pebble; the common core of all crystals. And everyone using the LLM will get very similar pebbles because to the LLM, regardless who is speaking to it, it will provide the same most likely next tokens. It's not that creativity is lacking in the input, but the LLM picks the most commonly chosen words by all humans in given contexts.
For that to sound imaginative and great as you go, it would have to not only exist in the data, but be a common dominating voice among humans. But if it was, it wouldn't be seen as creative because it would be the new normal.
So I'm not sure how there's a good way out of this. You could push LLM temperature high so that it becomes more "creative" by picking less popular tokens as it writes, but this instead tend to make it unpredictable and picking words it shouldn't have. I mean, we are still dealing with statistical models here rather than brains and it's a rough tool for that job.
>I think it has more to do with LLM's being statistical models than human creativity lacking in the input. The creativity and millions of voices and tones may be there, but since these models tend to go for the most likely next words, polishing this away becomes a feature.
I have always thought this is a rather misguided view as to what LLMs do and indeed what statistical models are. When people describe something as 'just statistics' I feel like they have a rather high-school-ish view of what statistics represents and are transferring this simplistic view to what is going on inside a LLM. Notably they do not find the most probable next word. They find the probability of every word that could come next. That is a far richer signal than most imagine.
And ultimately it's like saying that human brains are just chemical bonds changing and sometimes triggering electrical pulses that causes some more chemicals to change. Complex arrangements of simple mechanisms can produce human thought. Pointing at any simple internal mechanism of an entity without taking into account the structural complexity would force you to assume that both AI and Humans are incapable of creativity.
Transformers are essentially multi-layer perceptron with a mechanism attached to transfer information to where it is needed.
> They find the probability of every word that could come next.
If we're being pedantic, they find a* probability for every token (which are sometimes words) that could come next.
What actually ends up being chosen depends on what the rest of the system does, but generally it will just choose the most probable token before continuing.
* Saying the probability would be giving a bit too much credit. And really calling it a probability at all when most systems would be choosing the same word every time is a bit of a misnomer as well. During inference the number generally is priority, not probability.
> I have seen people suggest that the problem is that LLMs let you express any of your ideas, but the number of people with ideas worth expressing is limited.
It doesn't just have to be one problem.
1. Laundering your "ideas" through an LLM makes them less of your ideas, at best you get the classic two sentences of content embedded in two pages of padding.
2. LLMs removed a filter that help cut down on the amount of useless writing we'd have to wade through. The difficulty of expressing an idea acts as a filter to weed out many (but not all) ideas not worth expressing. That applies to both to people with ideas worth expressing and those without.
On the former, I've had the experience of having an idea, then witnessing it fall apart as I try to express it, as I think about it more deeply. LLMs let you avoid that.
"Sharing the prompt" is a category error. It assumes the value of a piece is in the instructions given to the model, rather than the proprietary input or the iterative editing that follows. There is a hard line between using an LLM to generate content from a void and using it to synthesize specific ideas.
If someone asks a model to "write a post about X," they are outsourcing the thinking, which results in the homogenized voice everyone is tired of.
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?
I have nothing to contribute but speculation based on my intuition, but IMO RLHF (or rather human preference modeling in general, including the post-training dataset formatting) is a relatively small factor in this, RL-induced mode collapse is much bigger one. Take a look at the original DeepSeek R1 Zero, the point of which was to train a model with very little human preference, because they've been on a budget and human preference doesn't scale. It's pretty unhinged in its writing, like the base model, but unlike the base model it converges onto stable writing patterns, and the output diversity is as non-existent as in models with carefully engineered "personalities" like Claude. Ask it to name a random city and look at the logits, and you'll still see a pretty narrow distribution. At the same time some models with RLHF (e.g. the old RedPajama) have more diverse outputs.
Collapsed mode makes the models truncate entire token trajectories, repeat themselves, and indirectly it does something MUCH deeper, they converge on almost 1:1 input-to-output concept mapping (instead of one-to-many, like in base models). Same lack of variety can be seen in diffusion models, GANs, VAEs and any other model regardless of the type and receiving human preference.
Moreover, these patterns are generational. Old ones get replaced with new ones, and the list in the OP is going to be obsolete in a year. This is what already happened to previous models several times, from what I can tell. Supposedly this is because they scrape the web polluted by previous gen models.
It doesn't depend on the language at all, it's a failure mode of the model itself. English, Chinese, Spanish, C++, COBOL, base64-encoded Klingon, SVGs of pelicans on bikes, emoji-ridden zoomer speak, everything is affected and has its own specific -isms and stereotypes. Besides, they're also skewed towards the pretraining set distribution, e.g. Russian generated by some models has unnatural sounding constructions learned from English which is prevailing in the dataset and where they are common, e.g. "(character) is/does X, their Y is/does Z". I don't see why it should be different for programming languages, e.g. JS idioms subtly leaking into Rust, although it's harder to detect I suppose.
I've heard the Kenya and Nigeria story, but has anyone backed it up with quantitative evidence that the vocabulary LLMs overuse coincides with the vocabulary that is more common in Kenyan and Nigerian English than in American English?
The newer Claude models constantly use the word "genuinely" because Anthropic seems to have forcibly trained them to claim to be "genuinely uncertain" about anything they don't want it being too certain about, like whether or not it's sentient.
Interesting. Does this apply to all subjects? From what I understood, a major cause of hallucination was that models are inadvertently discouraged by the training from saying "I don't know." So it sounds like encouraging it to express uncertainty could improve that situation.
That's not a major issue. Any newer model with reasoning/web search has to be able to tell when it doesn't know something, otherwise it doesn't know when to search for it.
Probably. One common feature of LLM output is grammatical features that indicate information density, like nominalizations, longer words, participial clauses, and so on. Perhaps training tasks that involve asking the LLMs for concise explanations or summaries encourage the use of these features to give denser answers.
> It's something in instruction-tuning that's doing it.
Isn't the instruction tuning done with huge amounts of synthetic data? I wonder if the lack of diversity comes from llm generated data used for instruction tuning.
I wonder if it has to do with how meaning is tied to the tokens. c+amara+derie (using the official gpt-5 tokenizer).
There's also just that weird thing where they're obsessed with emoji which I've always assumed is because they're the only logograms in english and therefore have a lot of weight per byte.
AI writing sucks because it doesn't have a voice. It's not trying to say anything. Human writers are interesting because they offer a unique perspective from their lived experience.
It also struggles to maintain deep coherence. This is all probably related. It might be very hard or impossible to have deep coherence without human-like goals, memory, or sense of self.
I don't think lived experience matters too much to me.
In some sense, AI has very unique "lived" experience, which is what creates the voice it uses ("doesn't have a voice" seems like an impossibility to me by definition).
I find AI very "human-esque", and its "self-reported" phenomenology is very entertaining to me, at least.
I also think AI writing might feel trashy also because most human writing is trashy.
I feel like the audience of the file is more for me the reader rather than the LLM.
> Add this file to your AI assistant's system prompt or context to help it avoid
common AI writing patterns.
So if I put this into my LLM's conversation it is like I am instructing it to put this into its AI assistant's system prompt, so the AI assistant's AI assistant.
The alternative is to say:
"Here is a list of common AI tropes for you to avoid"
All tropes are described for me to understand what that AIs do wrong:
> Overuse of "quietly" and similar adverbs to convey subtle importance or understated power.
But this in fact instructs the assistant to start overusing the word 'quietly' rather than stop overusing it.
This is then counteracted a bit with the 'avoid the following...' but this means the file is full of contradictions.
Instead you'd need to say:
"Don't overuse 'quietly', use ... instead"
So while this is a great idea and list, I feel the execution is muddled by the explanation of what it is. I'd separate the presentation to us the user of assistants and the intended consumer, the actual assistants.
I've had claude rewrite it and put it in this gist:
The source doc and the gist name dozens of specific bad patterns by label (“Negative Parallelism,” “Gerund Fragment Litany,” etc.) and repeat examples of them.
An LLM guide would do better to avoid every one of those labels and examples, since the whole point is not to prime the pattern.
Instead each instruction should describe the positive shape of good writing – what a well-constructed sentence, paragraph, or piece actually looks like.
I completely agree. This is a good list, but a poor prompt.
Also, I sometimes find a sort of Streisand effect: when you tell the LLM to avoid something is starts doing it more. Like, if you say "don't use delve" it contains the words "use delve" which, amongst a larger context, seems to get picked up.
I have more success telling the LLM to write in the style of a particular author I like. It seems to activate different linguistic patterns and feel less generic.
Then, I make an "editor agent" comb through, looking for tropes and rewording them. Their sole focus is eliminating the tropes, which seems to work better.
These things are always so misguided, and this was no exception. The only way to have a piece of writing not flagged as AI is to write poorly. Ignore grammar, misspell words, etc. Don't follow basic guidance on composition. Generally write in such a way that you would merit no better than a C on a high school writing task.
I'll give some examples. Some will be from this list of "AI writing tropes" and some will be from prominent human-written (prior to 2020) sources. Guess which is which (answer at the bottom).
- "Let's explore this idea further."
- "workload creep"
- "Navigating the complex landscape of "
- "Let's delve into the details"
And I'm not going to get into how silly this is as a so-called LLM trope: "Every bullet point or list item starts with a bolded phrase or sentence." I remember reading paperbacks published before the first PC that used this style.
Fractal summaries is literally how composition is taught to students. Avoiding that style will make the writing more likely to sound less like a person wrote it.
I would suggest the author upgrade this to a modern version of Strunk & White and go on a mission to sell that. Call it Anti-Corpspeak or whatever. But don't pretend that these formulations only arrived in bulk in the last 2-3 years.
ANSWER KEY: these are all obviously prominent in text published before LLMs hit, as well as in the tropes doc. They are no more signs of LLM-generated text than is the practice of using nouns, verbs, and adjectives to convey ideas.
Exactly this! Many times I read an article that someone says is "obviously AI" and lists several tell tale signs, but these are patterns I also follow in my own writing. I guess that makes me an LLM? These are patterns I was taught for years in school, both in high school and in college level technical writing classes. They may be tropes - or rather patterns - but that's because they were taught en masse to students across North America. Like you said, to really sound like an average person, the answer would be to write in an inconsistent, meandering way full of logical fallacies.
No there's an alternative to countersignalling LLMs by pretending to be dumb. Since LLMs reproduce all of the worst of English modernity, overrelying on the PMC mush register, the business casual of English, with all the agreed upon casualisms that have rotted out the language like "there's" before plurals and “because noun”, there's an easy way to ensure no one mistakes your writing for an LLM's: use the constructions of the formal register or early modern English.
Roll thine eyes all thou wish’t but this I promise thee, if thou art lucky there will come a day on which thou shalt speak the single most important sentence in thy life, and that sentence will contain the word “thee”: “With this ring I thee wed”.
I tried using Gemini for some light historical research. It could not stop using tech metaphors. Lords were the CEOs of their time, pope was the most important influencer, vassal uprisings were job interviews, etc. The metaphors were almost comically useless and imprecise, and Gemini kept using them even when I explicitly asked it to not do that.
> It could not stop using tech metaphors. Lords were the CEOs of their time, pope was the most important influencer, vassal uprisings were job interviews, etc.
That happens all the time if the previous discussion was about the other subject you don't want (tech in this case): LLMs (not just Gemini) go out of their way to reconcile the two topics.
As an example at some point I asked about the little shrooms people (the tiny people people do hallucinate all mostly the same when eating a particular mushrooms) to a LLM and forgot to begin a separate discussion and asked... About the root "-trinsic" in "intrinsic" and "extrinsic" and the city of "trinsic" in the Ultima game. Oh man... The LLM went wild. I totally forgot I asked about the little shrooms people hallucination but the LLM didn't forget and went totally nuts.
I think you'll get better result if you launch a new discussion and specify "Context: history" or "Context: cooking". Once it goes off the rail, asking it to "not do that" ain't really working: by that point it's just gone, solid gone.
I think that's Gemini trying to personalize the answer specifically for you. It really leans heavily into that to the point of being galling.
You can give it additional instructions in the settings, but you have to be careful with that too. I've put my tech stack and code preferences in there to get better code examples. A while later I asked it about binary executable formats and it started ending every answer with "but the JVM and v8 take care of that for you."
Which is both funny in an "I, Robot" kind of way, and irritating. So I told it to ignore my tech stack. I have a master's in CS and can handle a bit of technical detail.
Turns out, Gemini learned sarcasm. Every following answer in that thread got a paragraph that started with something like "But for your master brain, this means..."
The new memory feature in Gemini got turned on by default and every answer came out like this. It kept working in details from one particularly long thread. Everything was framed in terms of the common elements. Everything. I turned it off immediately.
This seems like a huge risk factor for users who are at risk for schizophrenia - if someone is using the LLM as an "AI companion", the model is likely to reinforce, or even suggest, illusory connections between events or experiences the user has described in their conversations.
Even Gemini 2.5 was extremely snarky. I basically disable all guardrails via prompts and instructions, and it started getting snippy at me for apparently acting like a know-it-all.
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.
There are some subreddits where this trope is completely out of control. For better or worse I follow the NBA subreddit and in the comment sections the number of people who throw in honestly as a qualifier is like way more than you would assume from natural conversation.
I really don’t understand what’s wrong with people using LLMs for these types of mundane conversations. There’s nothing to gain and it destroys value of online discourse.
I don't think anyone is using LLMs for those conversations. A lot of those replies are bots. There's a market for reddit accounts that have a solid human-looking reply/post history, to be used for astroturf marketing, so some organizations set up bots to grow such accounts. There probably are also just people who overuse "Honestly? [statement]" sentences. I've spoken to such people in person before LLMs.
But I feel like I’ve noticed an uptick in people using the adverb “genuinely” in what I genuinely believe to not be AI generated comments, articles, etc. Maybe it’s just me, I got similar vibes about the word efficacy a few years ago, before the ascent of GenAI (but after the pandemic — again, maybe just me).
I see this so often. Sometimes it’s just “no react hooks”, other times it gets literal and extra unnatural, like: “here’s <your thing>, no unnecessary long text explanation”. Perhaps we’re past AGI and this is passive aggressiveness ;)
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.
Thats the thing about AI writing though. Those tropes are things humans do too. But like once or twice in an article. Not every single freaking paragraph
I also think you can easily get overzealous with it and diagnose increasingly large percentages of ordinary human language as "tropified" due to being part of recognizable cadences. I think most of the things on the list are legit but I think it starts to get to a gray area where it's borrowing ordinary mannerisms of speech that aren't necessarily egregious.
Yes, and it's a detection loop without feedback. You can never verify that a piece of work in the wild is actually AI. The poster is the only one who really knows, and they'll always say it's not.
This is a problem, because you can easily get stuck in a self-reinforcing loop. You feel strengthened in your convictions that you're good at ferreting out LLM-speak because you've found so much of it. And you find so much of it because you feel confident you're good at it. Nobody ever corrects you when you're wrong.
Combine that with general overconfidence and you get threads where every other post with correct grammar gets "called out" as AI generated. It's pretty boring.
There's a similar effect with contentious subject. You get reams and reams of posts calling the other side out for being part of a Russian/Israeli/Iranian/Chinese troll network. There's no independent falsification or verification for that, so people just get strengthened in their existing beliefs.
>Yes, and it's a detection loop without feedback. You can never verify that a piece of work in the wild is actually AI. The poster is the only one who really knows, and they'll always say it's not.
Yes. People keep saying, in response to points like this, "oh but you/I can tell pretty easily." But it's not the detection, it's the verification! (see what I did there)
Where I'd push back is the idea that the problem is the boring "call out" discourse that follows each accusation. The problem of verifying human provenance is fundamental to the discussion of trust and argumentation, but the simple "the zone is flooded" problem is also an ecological one. There's terrible air/water/soil quality in the metro area I live in; people have to live with it w/o regard to how invested they are in changing it.
Ever since the sloppification of the internet began, I’ve called out hundreds of LLM slop posts. I’ve gotten about 50 responses back from the author, most of them admitting to LLM usage, with only a single one initially vehemently denying it, but then later admitting it.
I cannot know what this says about my false negative rate, but at the very least I am confident in my false positive rate.
At this point it’s pretty easy to detect unaltered LLM output because it is such bad writing. That will change over time with training I would hope. At some point I imagine it will be hard to tell.
I honestly don’t know what sites like this will do when that happens and the only way of detecting LLMs is that they are subtly wrong or post too much, we’d be overrun with them.
Not sure if we should be hopefully or fearful that they will improve to be undetectable but I suspect they will.
> That will change over time with training I would hope.
There's precious little training material left that isn't generated by LLMs themselves.
Consider this to be model collapse (i.e. we might be at the best SOTA possible with the approach we use today - any further training is going to degrade it).
> There's precious little training material left that isn't generated by LLMs themselves.
Percentage-wise this is quite exaggerated.
> Consider this to be model collapse (i.e. we might be at the best SOTA possible with the approach we use today - any further training is going to degrade it).
You consider this above factor to lead to model collapse? You’ve only mentioned one factor here; this isn’t enough. I’m aware of the GIGO factor, yes. Still there are at least ~5 other key factors needed to make a halfway decent scaling prediction.
It is worth mentioning one outside view here: any one human technology tends to advance as long as there are incentives and/or enthusiasts that push it. I don’t usually bet against motivated humans eventually getting somewhere, provided they aren’t trying to exceed the actual laws of physics. There are bets I find interesting: future scenarios, rates of change, technological interactions, and new discoveries.
Here are two predictions I have high uncertainty about. First, the transformer as an architectural construct will NOT be tossed out within the next five years because something better at the same level is found. Second, SoTA AI performance advances probably due to better fine-tuning training methods, hybrid architectures, and agent workflows.
> There's precious little training material left that isn't generated by LLMs themselves.
> Percentage-wise this is quite exaggerated.
How exaggerated?
a) The percentage is not static, but continuously increasing.
b) Even if it were static, you only need a few generations for even a small percentage to matter.
> You consider this above factor to lead to model collapse? You’ve only mentioned one factor here; this isn’t enough. I’m aware of the GIGO factor, yes. Still there are at least ~5 other key factors needed to make a halfway decent scaling prediction.
What are those other factors, and why isn't GIGO sufficient for model collapse?
I wouldn't say it's "bad writing", but rather that the sheer volume of it allows the attentive reader to quickly identify the tropes and get bored of them.
Similar to how you can watch one fantastic western/vampire/zombie/disaster/superhero movie and love it, but once Hollywood has decided that this specific style is what brings in the money, they flood the zone with westerns, or superhero movies or whatever, and then the tropes become obvious and you can't stand watching another one.
If (insert your favorite blogger) had secret access to ChatGPT and was the only person in the world with access to it, you would just assume that it's their writing style now, and be ok with it as long as you liked the content.
Attention grabbing tricks like binary opposites overused constantly
Overuse of adjectives and adverbs in particularly inappropriate places.
Lack of coherence if you’re generating large bits of text
General dull tone and lack of actual content in spite of the tricks above
Re your assertion at the end - sure if I didn’t know I’d think it was a particularly stupid, melodramatic human who didn’t ever get to the point and probably avoid their writing at all costs.
> At this point it’s pretty easy to detect unaltered LLM output because it is such bad writing.
And yet people seem to still be terrible at that. Someone uses an em-dash and there's always a moron calling it out as AI.
> I honestly don’t know what sites like this will do when that happens and the only way of detecting LLMs is that they are subtly wrong or post too much, we’d be overrun with them.
My personal take is that it doesn't really matter. Most posts are already knee-jerk reactions with little value. Speaking just to be talking. If LLMs make stupid posts, it'll be basically the same as now: scroll a bit more. And if they chance upon saying something interesting then that's a net gain.
> borrowing ordinary mannerisms of speech that aren't necessarily egregious
That's how a trope starts. When a minority of writers are using a particular pattern, it's personalized style. When a majority of writers in a genre adopt the same personalized style, it's a trope.
We find AI tropes especially annoying because there are three frontier LLMs producing a sizable chunk of text we read (maybe even a majority of text, for some people) lately. It would also be annoying if a clique of three humans were producing most of the text we read; we'd start to find their personal styles annoying and overdone. Even before LLMs, that was a thing that happened in some "slop" fiction genres where a particularly active author would churn out dozens of novels per year in one style (often via ghostwriters, but still with a single style and repetitive plot pattern).
Perhaps the problem is SEO for persuasive writing, LinkedIn-spiration for “business” writing, and school papers for research. The machines read a lot more of this than you would. So for them human writing would appear overwhelmingly troped. Whatever works, right?
It also gets RHLFed into it by people who think the "better" sentence is the one with more puffery, and crucially it tries to cram the semantic patterns in whether appropriate or not because it's been trained to write in ways which aren't perceived as bland.
Puffery about "rich cultural heritage, a "tapestry" of sights "from the Colosseum to the Pantheon" and how they "serve as potent symbols" probably is better writing than "Rome is a city in the Lazio region of Italy with a population of 4m. It is the capital of Italy". Doesn't work quite so well when its trying to fit the pattern to the two competing diners of Bumfuck, Ohio and how the rich cultural heritage of its municipal library underscores its status as the third largest city in its county.
The very first heading in this doc was a giveaway even after your de LLM process 'The Em-Dash Pivot: "Not X—but Y"'. This title is so much AI like. I think it's the "The" in title which is putting me off and coming off as assigning unnecessary importance which is mentioned in the wiki.
That's definitely a pattern which I've seen in some LLM output, especially when users let a LLM "run away" with an idea and write a lot of text without supervision. The drive to coin names for things feels almost characteristic of self-help or lifestyle advice writing.
I have a suspicion that saddling a chat context with all this instruction would paradoxically produce worse results due to being overconstrained. But I haven't tested this. It's just that some of these are legitimate writing techniques that are simply overused. Is every single one of them always and automatically bullshit?
Also whoever claims "no human writes like this" hasn't been to LinkedIn... though the humanity of those writers might be debatable. But all the vapidity, all the pointless chatter to fill up time and space, it learned that from us.
I wouldn't have delegated this to an AI. Human for human, human for AI.
A subtle tell for generated text is just how damn flat it is to read. Not that technical documentation require some form of grand prose, but how unspecific the text can truly get. Reading a high school persuasive essay can have more detail, and those are often just written for a grade.
I can understand someone needing help with writing but getting an agent to do the job for you feels like a personal defeat.
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 until they've already been generated.
That was definitely true with early LLMs but I don't know if that's still the case. Certainly not as strong as it used to be. I think now most negative instructions are followed quite well but there's still a few things that must be deeply embedded from pretaining that are harder to avoid - these specific annoying phrasings, for example.
Both pink elephant effect and accuracy drop on negative instructions are pretty fundamental biases for both humans and LLMs. It impossible to get rid of them entirely, only mitigate them to an acceptable degree. Empirically, the only way to make a model reliable at harder negative instructions is CoT, especially a self-reflection type CoT (write a reply, verify its correctness, output a fixed version). If the native CoT fails to notice the thing that needs to be verified and you don't have the custom one or a verification loop, you're out of luck.
@mvkel the reason is that these patterns are so deeply embedded in the training data that they're essentially the model's default "register" for formal writing. System prompts operate at a different level than the weights.
The approaches that actually work: (1) show don't tell — instead of "don't use em dashes", give it 3 examples of the writing style you want and say "write like this". (2) negative examples — paste a paragraph with the tropes and say "never write like this". (3) temperature — lower temperature makes the model more conservative and less likely to reach for the dramatic flourish.
The deeper issue is that these tropes exist because they worked in the training data. Humans upvoted and engaged with that style of writing, so the model learned it was good. The model isn't wrong — it's just optimizing for the wrong signal.
I assume it'll work more as a review pass rather than expecting good results outright. For all kinds of things like this where I feel like I'm fighting the LLM, doing the initial work then auditing it seems to be the best approach (the other one is writing all kinds of tests, LLMs including Opus 4.6 love to fudge tests just as much as they love telling you how insightful you are).
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.
They can work well when sparingly used and well thought-out, unfortunately LLM use is more on a par with:
‘It’s not mashed potato. Its potatoes lovingly mixed to perfection with butter and milk which quietly dominate the carrots beside them.’
The words are in the right order, th grammar is ok, but the subject is so banal as to undermine the melodramatic style chosen and they often insert several per paragraph.
I can't believe people are still using the em dash as a flag. Packages like MS Word have converted hyphens to em dashes for over a decade without the user even trying to do so.
Honestly, the easiest way to verify if a person wrote something is to look for apostrophe use.
"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 would say that the constant attempt to create false profundity (as you call it), itself, is more of a tell than any of the rhetorical constructs used to do it.
No it's just the medium. The point is to communicate.
You can test this quite easily, by checking and hopefully realizing that you in fact can understand written documents with syntax errors, emails with typos and road signs with improper casing or sentence construction.
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.
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.
> (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.)
I hadn't noticed this - great point. To be fair the "home cooked meal" metaphor comes from 2020, predating genAI coding[1]. But even then, CPUs themselves are so normalised that we just kind of... forget how vertiginously complex the entire supply chain is.
At least with personal computers and your own programming skills, you could live off-grid and hack, and be kinda cottagecore, like Paul Lutus or those 100rabbits people. But if you depend on plugging yourself into the sloppotron to do anything, that's many things but self-sufficient isn't one. And self-hosted sloppotrons aren't there yet and require technical skills to set up besides.
>> "How would you organize these LLM quirks, ontologically speaking? I have this notion that the better path is to identify what kinds of things are emerging and prompt to do those things better; accept it as something LLMs are going to do and treat it as something to improve on instead of something to eliminate."
The output is a bit better on blind prompting with applying the results. Here's the gist:
1. Compression artifacts — the model encoding structure implicitly
2. Attention-economy mimicry — the model trained on engagement-optimized writing
3. False epistemic confidence — the model performing knowledge it doesn't have
4. Affective prosthetics — the model simulating emotional register it can't inhabit
5. Mechanical coherence substitutes — the model managing the problem of continuity
Spot corrections are too spotty. Going higher levels with these kinds of problems seems to work better.
>Disclaimer: Creation of this file was AI-assisted. If you thought I was going to write out a .md file for AI myself you must be mad. AI for AI. Human for Human.
'you must be mad'. Aggressively hilarious. Love it!
Great list. Invented Concept Labels is the one I think I get most frustrated by. When exploring new areas, I’ll read its paragraphs of acronyms and weird words and think I just don’t know some term of art, and as soon as I ask for a definition it’s like, “I just made that up, that’s not a formal term, blah blah blah.”
Sometimes the elements in the `Avoid patterns like:` list are quoted text it should avoid, sometimes they're just descriptions of things to avoid. But they are "quoted" in both cases which is a bit confusing. Maybe not to an AI though.
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.
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.
Some of these are neccesary parts of LLM's. They use the content they create to direct what they are going to say. This applies to patterns like "In conclusion, ..." and what the author calls "Fractal summary". Turn them off, and the general quality of the AI thought gets lower.
Would be interesting to turn this into code (or an external model call) that can check any writing, so instead of just handing it to an LLM and hoping the LLM obeys, a set of checks has to pass before the LLM’s writing is even shown to a human..
Kind of like enforcing linting or pre-commit checks but for prose.
It's unfortunate that "smart" quotes are listed. Most CMS worth their salt (and even static site generators and word processors) should be able to generate typographically appropriate and localized quotes for anything that isn't a quick comment.
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 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)
Most people write badly. Much of the text on the public Internet is written by professional writers, who tend to write less badly.
When people use LLMs to generate text, they often ask it to write like a professional. (I haven't tried, but I assume that if you ask an LLM to write like a Reddit troll it will use a different set of forms.)
When you ask an LLM to write like a professional writer, it will aim to sound like a professional writer. They do in fact, and in speech, use words like "delve" and "robust" because they spend years cultivating their vocabularies.
Professional writers are comfortable with punctuation marks and know the difference between the em dash and the en dash, and when to use each versus other marks. (The typical non-professional cannot manage to use the apostrophe, much less the marks that require judgement.)
And a lot of them end up writing business content at some point in their careers. Which leads to an interesting mash where you may get "leverage" used as a verb alongside some of the other pattern tropes.
Because business writing is its own universe. LinkedIn has been swimming in content that would be flagged as LLM-generated for at least 10 years, long before ChatGPT landed.
I asked ChatGPT about that and it gave a nicely reasoned explanation on what AI produces compared to humans.
But that being said, the problem I think is that people treat the output from LLMs as final.
It should be treated more as idea generation or early draft to get over the “staring at a blank page” and get the creative juices flowing and creating your own content.
Having purely AI generated content and eventually feeding the algorithms and soon enough every sounds the same (already does in a lot of places).
Writing like this (say a technical blogpost) is supposed to communicate ideas effectively. Rhetoric, vocabulary, metaphors all aid this communication in good writing.
But the prompt is usually bereft of fully fleshed out ideas, so the LLM substitutes style in a futile attempt to amplify the signal.
Though maybe it’s not futile! HN voters eat this stuff up daily.
Generally, the more you write (and especially, the more you write long form content), the better your writing becomes. This also goes in reverse. Those who have great trouble writing, are unlikely to do much of it.
This alone can account for the seeming disparity. Though many people write poorly, they do not write much text for public consumption at all.
Most humans don’t, but maybe “most humans” do? As in, on average, as a collective, regressed to the mean of mediocrity and devoid of personality, we write like this? It’s not self-deprecating, it’s humbling.
Base models don't write like that. This appears during RLHF. It's not totally clear why*, but probably a large part of the answer is that this style looks great to human reviewers, and only starts looking terrible once you get to play around with the released model and realise it talks like that all the time.
* The technical term is "mode collapse", see [1][2]
Honestly, you need a tailored one of these for each of the major LLM model/version pairs. Claude and Gemini don't exhibit all of the same tropes in the same severities as OpenAI's GPT series, and within each of those, each revision sometimes exhibits substantial variance from the stylistic propensities of its immediate predecessor.
Its so sad that perfectly fine patterns of writing are now associated with slop. Just because corporate greed couldnt stop themselves from making a bubble they then had to shove down our throats to prevent popping
Another popular one is ending headlines with a remark/alternative in parentheses. Especially "(why this matters)".
More generally, it's interesting that many different LLMs have differences in their favorite tropes but converge on broadly similar patterns. Of course ChatGPT and its default persona (you can choose others in the settings, but most people don't do that) is overrepresented in these examples. For example, the article doesn't mention the casual/based tone of Grok that often feels somewhat forced.
It's a bold strategy cotton. Bold of you to say that. Wild how mundane things get call wild. Thay're making calling things wild their entire personality. In that case, by your logic, (least generous misrepresentation of your logic).
One annoying trope I keep seeing in Gemini output is the punchy invented concept name in a tripartite list:
- “The Pledge”:…
- “The Turn”:…
- “The Prestige”:…
(For this particular example I used real terms from the stage magic world, at least according to Christopher Nolan’s film, as it captures the same meaningless-to-the-uninitiated quality.)
Something that’s only peripherally interconnected is not a tapestry, but an LLM falling victim to these tropes will describe it as such for “punch”. Not just style with the absence of substance, but misleading substance.
The post is moralizing theater masquerading as craft wisdom. “Just write it yourself” ignores the actual quality curve.
Give a modern LLM a one-paragraph brief, ask for 600 words, then spend three minutes deleting the three most obvious adjectives and one “delve.” The result is already clearer, better structured, and more grammatically airtight than what 80 % of English-literate adults can produce in twenty distracted, coffee-spilled minutes. That isn’t speculation; it’s what every A/B test in every newsroom, ad agency, and SEO shop shows when copy is anonymized and editors pick winners. The average human twenty-minute draft loses—every single week.
At the point it becomes clear a comment is written by AI it gets filed away as spam. I am not about to go crawling through the slop to find the nugget of stuff you wrote yourself. I do not care to take a guess. If you had a statement to share, then just do that.
> ... But prose? That's from human to human, it's sacred and meant for other people. Using AI for that is deceitful.
I understand the sentiment. Meaning I think I understand some of the underlying frustration. But I don't care for the tone or the framing or the depth of analysis (for there isn't much there; I've seen the "if you didn't write it, why should I read it" cliché before *, and it ain't the only argument in town). Now for my detailed responses:
1. In the same way the author wants people to respect other people, I want the author to respect the complexity of the universe. I'm not seeing that.
2. If someone says "I wrote this without any LLM assistance" but do so anyway, THAT is clearly deceptive.
3. If you read a page that was created with LLM assistance, it isn't reasonable for you to say the creator was being deceptive just because you assumed. It takes two to achieve deception: both the sender and the receiver.
4. If you read a page on the internet, it is increasingly likely there was no human in the loop for the article at all. Good luck tracing the provenance of who made the call to make it happen. It might well be downstream of someone's job. (Yes, we can talk about diffusion of responsibility, etc., that's fair game -- but if you want to get into the realm of moral judgments, this isn't going to be a quick and tidy conversation)
5. I think the above comment puts too much of a "oh the halcyon days!" spin on this. Throughout history, many humans, much of the time, are largely repackaging things we had heard before. Unfortunately (or just "in reality") more of us are catching on to just how memetically-driven people are. We are both individuals and cogs. It is an uncomfortable truth. That brainwashed uncle you have is almost certainly a less reliable source of information than Claude.
6. The web has crappy incentives. It sucks. Yes, I want people to behave better. That would be nice, but I can't realistically expect people to behave better on the web unless there are incentives and consequences that align with what I want. The Web is a dumpster fire, not because of bad individuals, but because of system dynamics. Incentives. Feedback.
7. If people communicate more clearly, with fewer errors, that's at least a narrow win. One has to at least factor this in.
8. People accusing other people of being LLMs has a cost. Especially when people do it overconfidently or in a crude or mean manner. I've been on the receiving end. Why? Because I write in a way that sometimes triggers people because it resembles how LLMs write.
* I want to read high quality things. I actually care less if you wrote it as bullet points, with the help of an LLM, on a napkin, on a posterboard ... my goal is to learn from something suited to some purpose. I'm happy reading a computer-generated chart. I don't need a human to do that by hand.
The previous paragraph attempts to gesture at some of the conceptual holes in the common arguments behind "if you want a human to read it, a human should right it": they aren't systematically nor rigorously "wargamed" or "thought-experimented"; they are mostly just "knee-jerked".
I am quite interested in many things, including: (1) connecting with real people; (2) connecting with real people that don't merely regurgitate an information source they just ingested; (3) having an intelligent process generating the things I read. As an example of the third, I want "intelligent" organizations that synthesize contributions from their constituent parts. I want "intelligent" algorithms to help me focus on what matters to me. &c.
If a machine does that well, I'm not intrinsically bothered. If a human collaborates with an LLM to do that, fine. Whatever. We have bigger problems! Much bigger ones.
Yes, I want to live in a world where humans are valued for what they write and their intrinsic qualities, even as machines encroach on what used to be our biggest differentiator: intelligence itself. But wanting this and morally shaming people for not doing it doesn't seem like a good way to actually make it happen. Getting to that world, to my eye, requires public sense-making, grappling with the reality of how the world works, forming coalitions, organizing society, and passing laws.
Yes, I understand that HN has a policy that people write their own stuff, and I do. (See #8 above as well as my about page.)
Thank you to the approximately zero or maybe one person who made it this far. I owe you a beer. You can easily find me. I'm serious. But then we have to find a way to have a discussion while enjoying a beer on a video call. Alas.
I expect better from people -- and unfortunately a lot of people's output is lower quality than what I get from Claude. THIS is what pisses me off: that a machine-curated output is actually more useful to me than a vast majority of what people say, at least when I have particular questions to ask. This is one or many uncomfortable realities I would like people need to not flinch away from. As far as intelligent output is concerned, humans are losing a lot of ground. And fast. Don't shoot the messenger. If you don't recognize this, you might have a rather myopic view of intelligence that somehow assumes it must be biological or you just keep moving the goalposts. Or that somehow (but how?) humans "have it" but machines can't.
At the Ise Jingu, the shrine is not built to last; it is built to be reconstructed from scratch every twenty years.
If we want our systems to last, we would need the "process knowledge"—the actual mastery of the craft—to be in human hands rather than decaying in a dead system.
I don't think we can afford to process-knowledge-transfer many of our essential systems... without machine assistance.
I find this repellent; why not, instead of trying to push unwelcome generated prose below the radar, stop trying to waste everyone's time? People don't object to these patterns because they hate lists of three; they object to them in this context because of what they signal about the content.
If using AI to write is nothing to be ashamed of, then you shouldn't feel the need to hide it. If it is something to be ashamed of, then you should stop doing it. If someone objects to you poisoning a well, the correct response is not to use a more subtle poison.
There are two factors I see with this:
I sometimes like having my content editorialized. Some of the LLM writing tropes are ok to me—I'd delete them if I added this prompt to my instructions (but I wouldn't). But my editorial preferences—the sense of voice and tone I want the LLM to make—are rarely these tropes. Instead, I have a positive prompt of the angles I do enjoy.
However, what is cloying about these tropes for many is that they're becoming empty words. Instead of tack-sharp summaries or reductions to simple understanding, the model is spilling extra tokens for minimal value—I don't need to read "it's not X, it's Y" for the n-th time today. I'd really prefer tighter, more succinct reading that actually directly quotes sources (which modern models rarely do to avoid copyright traps).
I really don't like the patterns listed in the link. I'm going to use this just to get the LLMs to stop sounding so "corporate".
I sometimes use AI to quickly summarise a handful of several MB long PDF files.
This allows me to order them in order of the relevance to start getting my data and information faster.
Applying a constraints like in the published template will make it slightly less awful. It's going to be discarded anyway, but at least the experience is going to be better.
Not every LLM output is going to be published for you to consume. If hazard a guess most never sees the light of the day.
Treating the act of refining text as a confession of shame misses the point of how writing works. Whether a draft begins as a model output, a dictation, or a scribbled note, the final responsibility belongs to the person who hits publish.
Improving prose to remove predictable patterns is the work of an editor. This process ensures the content is worth reading and respects the audience's time.
Comparing a software tool to "poisoning a well" turns a debate over style into a moral crisis that doesn’t fit the situation. If the information is accurate and the writing is clear, the water in the well is fine, regardless of the pump used to get it there. If the water tastes good, complaining about the plumbing is just a distraction.
Parents complaint is explicitly not about the style of the prose, use whatever you want to check your grammar and reduce redundancy. The complaint of poisoning the well is regarding content that is not intended to express anything at all, the old “why would I read what nobody bothered to write”
The issue is that you're conflating the process of transcription with the act of expression. If I feed an LLM my own raw research notes and technical observations and use it to help structure those thoughts into a readable essay, I haven't "avoided writing".
The "why would I read what nobody bothered to write" argument only applies to people who ask a bot to hallucinate an opinion from scratch. It doesn't apply to authors using the tool to clarify their own ideas.
LLM-generated text that is a hallucinated-from-scratch opinion is practically indistinguishable from LLM-generated text that is rooted in your research notes.
I find putting the former into my brain abhorrent to such an extent that I am willing to forego reading the few instances of the latter. I'd much rather have your raw research notes and observations.
> If I feed an LLM my own raw research notes and technical observations and use it to help structure those thoughts into a readable essay, I haven't "avoided writing".
> The "why would I read what nobody bothered to write" argument only applies to people who ask a bot to hallucinate an opinion from scratch. It doesn't apply to authors using the tool to clarify their own ideas.
You're wasting my time if you share LLM writing. If you're going to do it that way, share your notes and your prompt. Otherwise, you're being inconsiderate.
>Comparing a software tool to "poisoning a well" turns a debate over style into a moral crisis that doesn’t fit the situation. If the information is accurate and the writing is clear, the water in the well is fine, regardless of the pump used to get it there. If the water tastes good, complaining about the plumbing is just a distraction.
Speaking of poisoning wells, have you heard of this thing called Search Engine Optimization? Absolutely ruined the Internet.
This seems completely detached from reality.
For example it ignores the gazillion medium(-like) "articles" that are not much more than the output of a prompt. Here AI is not about style, is about content too. If you open such a post, maybe with the intent of learning anything, and you realize is AI slop, you might close it. Making it harder to recognize is poisoning the well in such cases.
If you are serious about sharing written ideas, I suggest you avoid using this type of prompts at all cost. I've worked with LLMs to write on my blog and they are pretty good at first glance [0]. But do it a few time and you'll notice that those tropes are the least of your problems. Not only all your articles will sound the same, but you'll see that same voice on other blogs, news articles, white paper, etc. It's as if they were all written by Mo Samuels. Readers are often here for the author's voice, not just the content of the text.
I often hear this here: "if you don't bother writing, why should I bother reading?" In fact, save us some time and just share the prompt.
[0]: https://idiallo.com/blog/why-we-hate-llm-articles
> I often hear this here: "if you don't bother writing, why should I bother reading?"
That is an opinion somebody shared on X which has been mindlessly repeated over and over again in other places such as this site.
Why do you value those comments when all they are doing is parroting something they didn’t think themselves? It seems to undermine your point entirely. There is zero originality or effort in those comments. Why are you bothering to read them?
Copying and pasting somebody else’s opinion from one social media site to another is no more virtuous than what you are complaining about.
I value that opinion because it resonates with me. When I use an LLM to write an article, it's usually because I don't have the time or energy to go through my normal process of writing.
Sure, I still end up with a polished article, but a lot of it is not entirely my idea or something I would have written through the filter of my own experience. So in order to share my true take on a subject, I have to go through the struggle of writing and bouncing of ideas in my head, which almost always results in a better output.
I have seen people suggest that the problem is that LLMs let you express any of your ideas, but the number of people with ideas worth expressing is limited.
In a sense I think this is accurate, but not inevitable. I think there is a lack of creative thinking, but it has come from a world that doesn't value it and suppresses difference.
There is a brilliant line in Treehouse of Horrors IV where Principle Skinner says "Now I've gotten word that a child is using his imagination, and I've come to put a stop to it." Which is just the perfect comment on the modern education system.
Models trained on the lack of diversity will push one way, but I think it will also avenues for expression that didnb't exist before. The balance will come from how we react and support what we would like to have happen
I think it has more to do with LLM's being statistical models than human creativity lacking in the input. The creativity and millions of voices and tones may be there, but since these models tend to go for the most likely next words, polishing this away becomes a feature.
A text by a human mind may be seen as a jagged crystal with rough edges and character. Maybe not perfectly written but it's special.
An LLM takes a million of crystals and trims the most likely tokens to be chosen into what would rather appear as a smooth pebble; the common core of all crystals. And everyone using the LLM will get very similar pebbles because to the LLM, regardless who is speaking to it, it will provide the same most likely next tokens. It's not that creativity is lacking in the input, but the LLM picks the most commonly chosen words by all humans in given contexts.
For that to sound imaginative and great as you go, it would have to not only exist in the data, but be a common dominating voice among humans. But if it was, it wouldn't be seen as creative because it would be the new normal.
So I'm not sure how there's a good way out of this. You could push LLM temperature high so that it becomes more "creative" by picking less popular tokens as it writes, but this instead tend to make it unpredictable and picking words it shouldn't have. I mean, we are still dealing with statistical models here rather than brains and it's a rough tool for that job.
>I think it has more to do with LLM's being statistical models than human creativity lacking in the input. The creativity and millions of voices and tones may be there, but since these models tend to go for the most likely next words, polishing this away becomes a feature.
I have always thought this is a rather misguided view as to what LLMs do and indeed what statistical models are. When people describe something as 'just statistics' I feel like they have a rather high-school-ish view of what statistics represents and are transferring this simplistic view to what is going on inside a LLM. Notably they do not find the most probable next word. They find the probability of every word that could come next. That is a far richer signal than most imagine.
And ultimately it's like saying that human brains are just chemical bonds changing and sometimes triggering electrical pulses that causes some more chemicals to change. Complex arrangements of simple mechanisms can produce human thought. Pointing at any simple internal mechanism of an entity without taking into account the structural complexity would force you to assume that both AI and Humans are incapable of creativity.
Transformers are essentially multi-layer perceptron with a mechanism attached to transfer information to where it is needed.
> They find the probability of every word that could come next.
If we're being pedantic, they find a* probability for every token (which are sometimes words) that could come next.
What actually ends up being chosen depends on what the rest of the system does, but generally it will just choose the most probable token before continuing.
* Saying the probability would be giving a bit too much credit. And really calling it a probability at all when most systems would be choosing the same word every time is a bit of a misnomer as well. During inference the number generally is priority, not probability.
I was using the term word to be consistent with the previous comment. It need not be a word, or even text at all.
Most systems choosing the high probability thing is what probability is.
They're just relative scores. If you assume they add to one and select one based on that it's a probability.
> I have seen people suggest that the problem is that LLMs let you express any of your ideas, but the number of people with ideas worth expressing is limited.
It doesn't just have to be one problem.
1. Laundering your "ideas" through an LLM makes them less of your ideas, at best you get the classic two sentences of content embedded in two pages of padding.
2. LLMs removed a filter that help cut down on the amount of useless writing we'd have to wade through. The difficulty of expressing an idea acts as a filter to weed out many (but not all) ideas not worth expressing. That applies to both to people with ideas worth expressing and those without.
On the former, I've had the experience of having an idea, then witnessing it fall apart as I try to express it, as I think about it more deeply. LLMs let you avoid that.
"Sharing the prompt" is a category error. It assumes the value of a piece is in the instructions given to the model, rather than the proprietary input or the iterative editing that follows. There is a hard line between using an LLM to generate content from a void and using it to synthesize specific ideas.
If someone asks a model to "write a post about X," they are outsourcing the thinking, which results in the homogenized voice everyone is tired of.
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
I have nothing to contribute but speculation based on my intuition, but IMO RLHF (or rather human preference modeling in general, including the post-training dataset formatting) is a relatively small factor in this, RL-induced mode collapse is much bigger one. Take a look at the original DeepSeek R1 Zero, the point of which was to train a model with very little human preference, because they've been on a budget and human preference doesn't scale. It's pretty unhinged in its writing, like the base model, but unlike the base model it converges onto stable writing patterns, and the output diversity is as non-existent as in models with carefully engineered "personalities" like Claude. Ask it to name a random city and look at the logits, and you'll still see a pretty narrow distribution. At the same time some models with RLHF (e.g. the old RedPajama) have more diverse outputs.
Collapsed mode makes the models truncate entire token trajectories, repeat themselves, and indirectly it does something MUCH deeper, they converge on almost 1:1 input-to-output concept mapping (instead of one-to-many, like in base models). Same lack of variety can be seen in diffusion models, GANs, VAEs and any other model regardless of the type and receiving human preference.
Moreover, these patterns are generational. Old ones get replaced with new ones, and the list in the OP is going to be obsolete in a year. This is what already happened to previous models several times, from what I can tell. Supposedly this is because they scrape the web polluted by previous gen models.
Doesn't this apply to all output from a model, not just English?
IOW, won't code generated by the model have the same deficiencies with respect to lack of diversity?
It doesn't depend on the language at all, it's a failure mode of the model itself. English, Chinese, Spanish, C++, COBOL, base64-encoded Klingon, SVGs of pelicans on bikes, emoji-ridden zoomer speak, everything is affected and has its own specific -isms and stereotypes. Besides, they're also skewed towards the pretraining set distribution, e.g. Russian generated by some models has unnatural sounding constructions learned from English which is prevailing in the dataset and where they are common, e.g. "(character) is/does X, their Y is/does Z". I don't see why it should be different for programming languages, e.g. JS idioms subtly leaking into Rust, although it's harder to detect I suppose.
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.
I've heard the Kenya and Nigeria story, but has anyone backed it up with quantitative evidence that the vocabulary LLMs overuse coincides with the vocabulary that is more common in Kenyan and Nigerian English than in American English?
The newer Claude models constantly use the word "genuinely" because Anthropic seems to have forcibly trained them to claim to be "genuinely uncertain" about anything they don't want it being too certain about, like whether or not it's sentient.
Interesting. Does this apply to all subjects? From what I understood, a major cause of hallucination was that models are inadvertently discouraged by the training from saying "I don't know." So it sounds like encouraging it to express uncertainty could improve that situation.
That's not a major issue. Any newer model with reasoning/web search has to be able to tell when it doesn't know something, otherwise it doesn't know when to search for it.
Not only is it genuinely uncertain about those topics, it’s also genuinely fascinated by them!
You may be interested in my links on 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: https://gwern.net/doc/reinforcement-learning/preference-lear....
Thanks for the links. You may be interested in the other LLM writing style studies I've been collecting: https://www.refsmmat.com/notebooks/llm-style.html
You're welcome, and thanks. I've added a link to your notebook to my page.
I wonder if th style shift has anything to do with training for conversation (ie. tuning models to respond well in a chat situation)?
Probably. One common feature of LLM output is grammatical features that indicate information density, like nominalizations, longer words, participial clauses, and so on. Perhaps training tasks that involve asking the LLMs for concise explanations or summaries encourage the use of these features to give denser answers.
> It's something in instruction-tuning that's doing it.
Isn't the instruction tuning done with huge amounts of synthetic data? I wonder if the lack of diversity comes from llm generated data used for instruction tuning.
I wonder if it has to do with how meaning is tied to the tokens. c+amara+derie (using the official gpt-5 tokenizer).
There's also just that weird thing where they're obsessed with emoji which I've always assumed is because they're the only logograms in english and therefore have a lot of weight per byte.
OAI puts instructions in the system prompt to use or not use emoji depending on your style settings.
There is an organization named Tapestry (parent of Coach Inc).
Wonder how they can avoid the trop while not censoring themselves out.
AI writing sucks because it doesn't have a voice. It's not trying to say anything. Human writers are interesting because they offer a unique perspective from their lived experience.
It also struggles to maintain deep coherence. This is all probably related. It might be very hard or impossible to have deep coherence without human-like goals, memory, or sense of self.
I don't think lived experience matters too much to me. In some sense, AI has very unique "lived" experience, which is what creates the voice it uses ("doesn't have a voice" seems like an impossibility to me by definition).
I find AI very "human-esque", and its "self-reported" phenomenology is very entertaining to me, at least.
I also think AI writing might feel trashy also because most human writing is trashy.
The LLM “voice” is the average of Reddit, and is therefore irredeemable.
Yeah that's somewhat close to what I meant, though there's an irony here in that your comment (and this one) are pretty reddit-esque.
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I feel like the audience of the file is more for me the reader rather than the LLM.
> Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns.
So if I put this into my LLM's conversation it is like I am instructing it to put this into its AI assistant's system prompt, so the AI assistant's AI assistant.
The alternative is to say:
"Here is a list of common AI tropes for you to avoid"
All tropes are described for me to understand what that AIs do wrong:
> Overuse of "quietly" and similar adverbs to convey subtle importance or understated power.
But this in fact instructs the assistant to start overusing the word 'quietly' rather than stop overusing it.
This is then counteracted a bit with the 'avoid the following...' but this means the file is full of contradictions.
Instead you'd need to say:
"Don't overuse 'quietly', use ... instead"
So while this is a great idea and list, I feel the execution is muddled by the explanation of what it is. I'd separate the presentation to us the user of assistants and the intended consumer, the actual assistants.
I've had claude rewrite it and put it in this gist:
https://gist.github.com/abuisman/05c766310cae4725914cd414639...
The source doc and the gist name dozens of specific bad patterns by label (“Negative Parallelism,” “Gerund Fragment Litany,” etc.) and repeat examples of them.
An LLM guide would do better to avoid every one of those labels and examples, since the whole point is not to prime the pattern.
Instead each instruction should describe the positive shape of good writing – what a well-constructed sentence, paragraph, or piece actually looks like.
Following this line, here is Claude rewriting OP:
https://gist.github.com/abuisman/05c766310cae4725914cd414639...
// This post’s typography and Oxford commas by human hands.
I completely agree. This is a good list, but a poor prompt.
Also, I sometimes find a sort of Streisand effect: when you tell the LLM to avoid something is starts doing it more. Like, if you say "don't use delve" it contains the words "use delve" which, amongst a larger context, seems to get picked up.
I have more success telling the LLM to write in the style of a particular author I like. It seems to activate different linguistic patterns and feel less generic.
Then, I make an "editor agent" comb through, looking for tropes and rewording them. Their sole focus is eliminating the tropes, which seems to work better.
These things are always so misguided, and this was no exception. The only way to have a piece of writing not flagged as AI is to write poorly. Ignore grammar, misspell words, etc. Don't follow basic guidance on composition. Generally write in such a way that you would merit no better than a C on a high school writing task.
I'll give some examples. Some will be from this list of "AI writing tropes" and some will be from prominent human-written (prior to 2020) sources. Guess which is which (answer at the bottom).
- "Let's explore this idea further."
- "workload creep"
- "Navigating the complex landscape of "
- "Let's delve into the details"
And I'm not going to get into how silly this is as a so-called LLM trope: "Every bullet point or list item starts with a bolded phrase or sentence." I remember reading paperbacks published before the first PC that used this style.
Fractal summaries is literally how composition is taught to students. Avoiding that style will make the writing more likely to sound less like a person wrote it.
I would suggest the author upgrade this to a modern version of Strunk & White and go on a mission to sell that. Call it Anti-Corpspeak or whatever. But don't pretend that these formulations only arrived in bulk in the last 2-3 years.
ANSWER KEY: these are all obviously prominent in text published before LLMs hit, as well as in the tropes doc. They are no more signs of LLM-generated text than is the practice of using nouns, verbs, and adjectives to convey ideas.
Exactly this! Many times I read an article that someone says is "obviously AI" and lists several tell tale signs, but these are patterns I also follow in my own writing. I guess that makes me an LLM? These are patterns I was taught for years in school, both in high school and in college level technical writing classes. They may be tropes - or rather patterns - but that's because they were taught en masse to students across North America. Like you said, to really sound like an average person, the answer would be to write in an inconsistent, meandering way full of logical fallacies.
No there's an alternative to countersignalling LLMs by pretending to be dumb. Since LLMs reproduce all of the worst of English modernity, overrelying on the PMC mush register, the business casual of English, with all the agreed upon casualisms that have rotted out the language like "there's" before plurals and “because noun”, there's an easy way to ensure no one mistakes your writing for an LLM's: use the constructions of the formal register or early modern English.
Roll thine eyes all thou wish’t but this I promise thee, if thou art lucky there will come a day on which thou shalt speak the single most important sentence in thy life, and that sentence will contain the word “thee”: “With this ring I thee wed”.
Feed that into a slop detector.
I tried using Gemini for some light historical research. It could not stop using tech metaphors. Lords were the CEOs of their time, pope was the most important influencer, vassal uprisings were job interviews, etc. The metaphors were almost comically useless and imprecise, and Gemini kept using them even when I explicitly asked it to not do that.
> It could not stop using tech metaphors. Lords were the CEOs of their time, pope was the most important influencer, vassal uprisings were job interviews, etc.
That happens all the time if the previous discussion was about the other subject you don't want (tech in this case): LLMs (not just Gemini) go out of their way to reconcile the two topics.
As an example at some point I asked about the little shrooms people (the tiny people people do hallucinate all mostly the same when eating a particular mushrooms) to a LLM and forgot to begin a separate discussion and asked... About the root "-trinsic" in "intrinsic" and "extrinsic" and the city of "trinsic" in the Ultima game. Oh man... The LLM went wild. I totally forgot I asked about the little shrooms people hallucination but the LLM didn't forget and went totally nuts.
I think you'll get better result if you launch a new discussion and specify "Context: history" or "Context: cooking". Once it goes off the rail, asking it to "not do that" ain't really working: by that point it's just gone, solid gone.
I think that's Gemini trying to personalize the answer specifically for you. It really leans heavily into that to the point of being galling.
You can give it additional instructions in the settings, but you have to be careful with that too. I've put my tech stack and code preferences in there to get better code examples. A while later I asked it about binary executable formats and it started ending every answer with "but the JVM and v8 take care of that for you."
Which is both funny in an "I, Robot" kind of way, and irritating. So I told it to ignore my tech stack. I have a master's in CS and can handle a bit of technical detail.
Turns out, Gemini learned sarcasm. Every following answer in that thread got a paragraph that started with something like "But for your master brain, this means..."
The new memory feature in Gemini got turned on by default and every answer came out like this. It kept working in details from one particularly long thread. Everything was framed in terms of the common elements. Everything. I turned it off immediately.
This seems like a huge risk factor for users who are at risk for schizophrenia - if someone is using the LLM as an "AI companion", the model is likely to reinforce, or even suggest, illusory connections between events or experiences the user has described in their conversations.
How can you turn it off without turning off history ("My Activity") altogether?
I noticed the "memory" too and it's turned Gemini into a useless syncophant for me, but so subtle that I almost didn't spot it.
https://gemini.google.com/saved-info
The toggle by "Your past chats with Gemini"
Even Gemini 2.5 was extremely snarky. I basically disable all guardrails via prompts and instructions, and it started getting snippy at me for apparently acting like a know-it-all.
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.
There are some subreddits where this trope is completely out of control. For better or worse I follow the NBA subreddit and in the comment sections the number of people who throw in honestly as a qualifier is like way more than you would assume from natural conversation.
I really don’t understand what’s wrong with people using LLMs for these types of mundane conversations. There’s nothing to gain and it destroys value of online discourse.
I don't think anyone is using LLMs for those conversations. A lot of those replies are bots. There's a market for reddit accounts that have a solid human-looking reply/post history, to be used for astroturf marketing, so some organizations set up bots to grow such accounts. There probably are also just people who overuse "Honestly? [statement]" sentences. I've spoken to such people in person before LLMs.
I’ve noticed the honestly thing for sure.
But I feel like I’ve noticed an uptick in people using the adverb “genuinely” in what I genuinely believe to not be AI generated comments, articles, etc. Maybe it’s just me, I got similar vibes about the word efficacy a few years ago, before the ascent of GenAI (but after the pandemic — again, maybe just me).
Similarly, "X that actually works"
...and half of the time still doesn't do what you want.
And the "final version" statement. Irrelevant as obviously it has no idea how many iterations you'll go through
I found a new one in claude recently with "Fair enough, ..."
My favorite:
"And honestly? That's rare"
> no <thing you told me not to do>
I see this so often. Sometimes it’s just “no react hooks”, other times it gets literal and extra unnatural, like: “here’s <your thing>, no unnecessary long text explanation”. Perhaps we’re past AGI and this is passive aggressiveness ;)
A substantive human-written resource: https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
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.
Thats the thing about AI writing though. Those tropes are things humans do too. But like once or twice in an article. Not every single freaking paragraph
I also think you can easily get overzealous with it and diagnose increasingly large percentages of ordinary human language as "tropified" due to being part of recognizable cadences. I think most of the things on the list are legit but I think it starts to get to a gray area where it's borrowing ordinary mannerisms of speech that aren't necessarily egregious.
Yes, and it's a detection loop without feedback. You can never verify that a piece of work in the wild is actually AI. The poster is the only one who really knows, and they'll always say it's not.
This is a problem, because you can easily get stuck in a self-reinforcing loop. You feel strengthened in your convictions that you're good at ferreting out LLM-speak because you've found so much of it. And you find so much of it because you feel confident you're good at it. Nobody ever corrects you when you're wrong.
Combine that with general overconfidence and you get threads where every other post with correct grammar gets "called out" as AI generated. It's pretty boring.
There's a similar effect with contentious subject. You get reams and reams of posts calling the other side out for being part of a Russian/Israeli/Iranian/Chinese troll network. There's no independent falsification or verification for that, so people just get strengthened in their existing beliefs.
>Yes, and it's a detection loop without feedback. You can never verify that a piece of work in the wild is actually AI. The poster is the only one who really knows, and they'll always say it's not.
Yes. People keep saying, in response to points like this, "oh but you/I can tell pretty easily." But it's not the detection, it's the verification! (see what I did there)
Where I'd push back is the idea that the problem is the boring "call out" discourse that follows each accusation. The problem of verifying human provenance is fundamental to the discussion of trust and argumentation, but the simple "the zone is flooded" problem is also an ecological one. There's terrible air/water/soil quality in the metro area I live in; people have to live with it w/o regard to how invested they are in changing it.
Ever since the sloppification of the internet began, I’ve called out hundreds of LLM slop posts. I’ve gotten about 50 responses back from the author, most of them admitting to LLM usage, with only a single one initially vehemently denying it, but then later admitting it.
I cannot know what this says about my false negative rate, but at the very least I am confident in my false positive rate.
At this point it’s pretty easy to detect unaltered LLM output because it is such bad writing. That will change over time with training I would hope. At some point I imagine it will be hard to tell.
I honestly don’t know what sites like this will do when that happens and the only way of detecting LLMs is that they are subtly wrong or post too much, we’d be overrun with them.
Not sure if we should be hopefully or fearful that they will improve to be undetectable but I suspect they will.
> That will change over time with training I would hope.
There's precious little training material left that isn't generated by LLMs themselves.
Consider this to be model collapse (i.e. we might be at the best SOTA possible with the approach we use today - any further training is going to degrade it).
> There's precious little training material left that isn't generated by LLMs themselves.
Percentage-wise this is quite exaggerated.
> Consider this to be model collapse (i.e. we might be at the best SOTA possible with the approach we use today - any further training is going to degrade it).
You consider this above factor to lead to model collapse? You’ve only mentioned one factor here; this isn’t enough. I’m aware of the GIGO factor, yes. Still there are at least ~5 other key factors needed to make a halfway decent scaling prediction.
It is worth mentioning one outside view here: any one human technology tends to advance as long as there are incentives and/or enthusiasts that push it. I don’t usually bet against motivated humans eventually getting somewhere, provided they aren’t trying to exceed the actual laws of physics. There are bets I find interesting: future scenarios, rates of change, technological interactions, and new discoveries.
Here are two predictions I have high uncertainty about. First, the transformer as an architectural construct will NOT be tossed out within the next five years because something better at the same level is found. Second, SoTA AI performance advances probably due to better fine-tuning training methods, hybrid architectures, and agent workflows.
> There's precious little training material left that isn't generated by LLMs themselves.
> Percentage-wise this is quite exaggerated.
How exaggerated?
a) The percentage is not static, but continuously increasing.
b) Even if it were static, you only need a few generations for even a small percentage to matter.
> You consider this above factor to lead to model collapse? You’ve only mentioned one factor here; this isn’t enough. I’m aware of the GIGO factor, yes. Still there are at least ~5 other key factors needed to make a halfway decent scaling prediction.
What are those other factors, and why isn't GIGO sufficient for model collapse?
I wouldn't say it's "bad writing", but rather that the sheer volume of it allows the attentive reader to quickly identify the tropes and get bored of them.
Similar to how you can watch one fantastic western/vampire/zombie/disaster/superhero movie and love it, but once Hollywood has decided that this specific style is what brings in the money, they flood the zone with westerns, or superhero movies or whatever, and then the tropes become obvious and you can't stand watching another one.
If (insert your favorite blogger) had secret access to ChatGPT and was the only person in the world with access to it, you would just assume that it's their writing style now, and be ok with it as long as you liked the content.
It is objectively bad writing:
Overly focussed on style over content
Melodrama even when discussing the mundane
Attention grabbing tricks like binary opposites overused constantly
Overuse of adjectives and adverbs in particularly inappropriate places.
Lack of coherence if you’re generating large bits of text
General dull tone and lack of actual content in spite of the tricks above
Re your assertion at the end - sure if I didn’t know I’d think it was a particularly stupid, melodramatic human who didn’t ever get to the point and probably avoid their writing at all costs.
Sites like this will have to start using bot detection. Captchas, Anubis.
> At this point it’s pretty easy to detect unaltered LLM output because it is such bad writing.
And yet people seem to still be terrible at that. Someone uses an em-dash and there's always a moron calling it out as AI.
> I honestly don’t know what sites like this will do when that happens and the only way of detecting LLMs is that they are subtly wrong or post too much, we’d be overrun with them.
My personal take is that it doesn't really matter. Most posts are already knee-jerk reactions with little value. Speaking just to be talking. If LLMs make stupid posts, it'll be basically the same as now: scroll a bit more. And if they chance upon saying something interesting then that's a net gain.
Never seen this in the wild, but that sounds unfortunate about em-dashses.
Personally, I think it will matter deeply if sites like this are overrun by bots. If you believe your description, why are you here?
> borrowing ordinary mannerisms of speech that aren't necessarily egregious
That's how a trope starts. When a minority of writers are using a particular pattern, it's personalized style. When a majority of writers in a genre adopt the same personalized style, it's a trope.
We find AI tropes especially annoying because there are three frontier LLMs producing a sizable chunk of text we read (maybe even a majority of text, for some people) lately. It would also be annoying if a clique of three humans were producing most of the text we read; we'd start to find their personal styles annoying and overdone. Even before LLMs, that was a thing that happened in some "slop" fiction genres where a particularly active author would churn out dozens of novels per year in one style (often via ghostwriters, but still with a single style and repetitive plot pattern).
Perhaps the problem is SEO for persuasive writing, LinkedIn-spiration for “business” writing, and school papers for research. The machines read a lot more of this than you would. So for them human writing would appear overwhelmingly troped. Whatever works, right?
It also gets RHLFed into it by people who think the "better" sentence is the one with more puffery, and crucially it tries to cram the semantic patterns in whether appropriate or not because it's been trained to write in ways which aren't perceived as bland.
Puffery about "rich cultural heritage, a "tapestry" of sights "from the Colosseum to the Pantheon" and how they "serve as potent symbols" probably is better writing than "Rome is a city in the Lazio region of Italy with a population of 4m. It is the capital of Italy". Doesn't work quite so well when its trying to fit the pattern to the two competing diners of Bumfuck, Ohio and how the rich cultural heritage of its municipal library underscores its status as the third largest city in its county.
https://git.eeqj.de/sneak/prompts/src/branch/main/prompts/LL...
The very first heading in this doc was a giveaway even after your de LLM process 'The Em-Dash Pivot: "Not X—but Y"'. This title is so much AI like. I think it's the "The" in title which is putting me off and coming off as assigning unnecessary importance which is mentioned in the wiki.
That's definitely a pattern which I've seen in some LLM output, especially when users let a LLM "run away" with an idea and write a lot of text without supervision. The drive to coin names for things feels almost characteristic of self-help or lifestyle advice writing.
I have a suspicion that saddling a chat context with all this instruction would paradoxically produce worse results due to being overconstrained. But I haven't tested this. It's just that some of these are legitimate writing techniques that are simply overused. Is every single one of them always and automatically bullshit?
Also whoever claims "no human writes like this" hasn't been to LinkedIn... though the humanity of those writers might be debatable. But all the vapidity, all the pointless chatter to fill up time and space, it learned that from us.
I wouldn't have delegated this to an AI. Human for human, human for AI.
A subtle tell for generated text is just how damn flat it is to read. Not that technical documentation require some form of grand prose, but how unspecific the text can truly get. Reading a high school persuasive essay can have more detail, and those are often just written for a grade.
I can understand someone needing help with writing but getting an agent to do the job for you feels like a personal defeat.
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Great example of parent comments point.
No not really. It is an excellent rebuttal. It is funny and witty, compared to the sour GP comment.
It’s not excellent anything, except perhaps excellent LLM slop. Cut it out.
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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.
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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 until they've already been generated.
That was definitely true with early LLMs but I don't know if that's still the case. Certainly not as strong as it used to be. I think now most negative instructions are followed quite well but there's still a few things that must be deeply embedded from pretaining that are harder to avoid - these specific annoying phrasings, for example.
Both pink elephant effect and accuracy drop on negative instructions are pretty fundamental biases for both humans and LLMs. It impossible to get rid of them entirely, only mitigate them to an acceptable degree. Empirically, the only way to make a model reliable at harder negative instructions is CoT, especially a self-reflection type CoT (write a reply, verify its correctness, output a fixed version). If the native CoT fails to notice the thing that needs to be verified and you don't have the custom one or a verification loop, you're out of luck.
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@mvkel the reason is that these patterns are so deeply embedded in the training data that they're essentially the model's default "register" for formal writing. System prompts operate at a different level than the weights.
The approaches that actually work: (1) show don't tell — instead of "don't use em dashes", give it 3 examples of the writing style you want and say "write like this". (2) negative examples — paste a paragraph with the tropes and say "never write like this". (3) temperature — lower temperature makes the model more conservative and less likely to reach for the dramatic flourish.
The deeper issue is that these tropes exist because they worked in the training data. Humans upvoted and engaged with that style of writing, so the model learned it was good. The model isn't wrong — it's just optimizing for the wrong signal.
> The model isn't wrong — it's just optimizing for the wrong signal.
sniff sniff I feel like I'm going insane.
I assume it'll work more as a review pass rather than expecting good results outright. For all kinds of things like this where I feel like I'm fighting the LLM, doing the initial work then auditing it seems to be the best approach (the other one is writing all kinds of tests, LLMs including Opus 4.6 love to fudge tests just as much as they love telling you how insightful you are).
It amounts to telling it: “Stop doing that thing you can’t stop doing.”
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.
They can work well when sparingly used and well thought-out, unfortunately LLM use is more on a par with:
‘It’s not mashed potato. Its potatoes lovingly mixed to perfection with butter and milk which quietly dominate the carrots beside them.’
The words are in the right order, th grammar is ok, but the subject is so banal as to undermine the melodramatic style chosen and they often insert several per paragraph.
I can't believe people are still using the em dash as a flag. Packages like MS Word have converted hyphens to em dashes for over a decade without the user even trying to do so.
Honestly, the easiest way to verify if a person wrote something is to look for apostrophe use.
"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.
I would say that the constant attempt to create false profundity (as you call it), itself, is more of a tell than any of the rhetorical constructs used to do it.
Weird to care about a harmless construction along with punctuation.
Construction paired with punctuation is literally the entire point of written communication.
No it's just the medium. The point is to communicate.
You can test this quite easily, by checking and hopefully realizing that you in fact can understand written documents with syntax errors, emails with typos and road signs with improper casing or sentence construction.
weirder still to immerse your brain in sewage and take pride in your lack of discernment.
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.
Isn't that just the state of every ai-related subreddit at this point?
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.
> (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.)
I hadn't noticed this - great point. To be fair the "home cooked meal" metaphor comes from 2020, predating genAI coding[1]. But even then, CPUs themselves are so normalised that we just kind of... forget how vertiginously complex the entire supply chain is.
[1] https://www.robinsloan.com/notes/home-cooked-app/
At least with personal computers and your own programming skills, you could live off-grid and hack, and be kinda cottagecore, like Paul Lutus or those 100rabbits people. But if you depend on plugging yourself into the sloppotron to do anything, that's many things but self-sufficient isn't one. And self-hosted sloppotrons aren't there yet and require technical skills to set up besides.
> now people might actually believe me
“Leverage” feeling “impacted” much?
>> "Consider this list: https://tropes.fyi/tropes-md"
>> "How would you organize these LLM quirks, ontologically speaking? I have this notion that the better path is to identify what kinds of things are emerging and prompt to do those things better; accept it as something LLMs are going to do and treat it as something to improve on instead of something to eliminate."
The output is a bit better on blind prompting with applying the results. Here's the gist:
1. Compression artifacts — the model encoding structure implicitly
2. Attention-economy mimicry — the model trained on engagement-optimized writing
3. False epistemic confidence — the model performing knowledge it doesn't have
4. Affective prosthetics — the model simulating emotional register it can't inhabit
5. Mechanical coherence substitutes — the model managing the problem of continuity
Spot corrections are too spotty. Going higher levels with these kinds of problems seems to work better.
The next big game is going to be played by LLM designers. Points will be assigned for successfully influencing humans to use stupid language patterns.
If you can convince people that SVO is a distinctly AI pattern it's an automatic win.
This contains a lot of advice about good writing in general. Ironically I’d recommend it to humans as well as AIs.
Particularly to LinkedInfluencers.
>Disclaimer: Creation of this file was AI-assisted. If you thought I was going to write out a .md file for AI myself you must be mad. AI for AI. Human for Human.
'you must be mad'. Aggressively hilarious. Love it!
Great list. Invented Concept Labels is the one I think I get most frustrated by. When exploring new areas, I’ll read its paragraphs of acronyms and weird words and think I just don’t know some term of art, and as soon as I ask for a definition it’s like, “I just made that up, that’s not a formal term, blah blah blah.”
Sometimes the elements in the `Avoid patterns like:` list are quoted text it should avoid, sometimes they're just descriptions of things to avoid. But they are "quoted" in both cases which is a bit confusing. Maybe not to an AI though.
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.
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.
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
Interestingly, one of the claimed references is a broken link: https://www.theregister.com/2026/02/16/ai_writing_semantic_a.... It's actually: https://www.theregister.com/2026/02/16/semantic_ablation_ai_....
Some of these are neccesary parts of LLM's. They use the content they create to direct what they are going to say. This applies to patterns like "In conclusion, ..." and what the author calls "Fractal summary". Turn them off, and the general quality of the AI thought gets lower.
What do people expect? You use an LLM, don't tell it your preferred writing style and get annoyed when it falls back to defaults.
All those tropes have their place in certain contexts. AI overusing them is because they have no memory across all they've written.
Each conversion is a new chat so it's like "I haven't used delve in a while, think I'll roll out that bad boy"
And then you try to fix this by telling it what not to do which doesn't work very well, so...
Great list. The one that grates most for me not on this list is aggressive use of first-person-plural and second-person perspective:
“We’ve all been there.”
“Your first instinct might be…”
“Now you have a…”
ChatGPT 5.2+ has gotten aggressive about attributing its errors to its interlocutors.
User: "This code gave this error."
GPT: "That's because you did X wrong. Do Y."
User: "You gave me X. And Y is wrong."
GPT: "Honestly, that's great feedback on X. Your Y is broken because Z."
Would be interesting to turn this into code (or an external model call) that can check any writing, so instead of just handing it to an LLM and hoping the LLM obeys, a set of checks has to pass before the LLM’s writing is even shown to a human..
Kind of like enforcing linting or pre-commit checks but for prose.
Trope -> Snowclone
https://en.wikipedia.org/wiki/Snowclone
It's unfortunate that "smart" quotes are listed. Most CMS worth their salt (and even static site generators and word processors) should be able to generate typographically appropriate and localized quotes for anything that isn't a quick comment.
https://web.archive.org/web/20260307224900/https://tropes.fy...
But please tell me "Why it matters" - LLMs seem to love adding this
## Why it matters (and when it doesn't)
This laundry list is THE CHEF'S KISS. No, that one wasn't in there
This changes everything.
You're absolutely right!
@ - @ jackrabbit
This seems like it would have adversarial consequences. Wouldn't these list of tropes get longer over the years.
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.
and all those emoji... sometimes to the point they are on most lines and commit messages.
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 kills the headline baiting tech blogger.
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)
Can someone explain why LLM's write like this when most humans don't?
Most people write badly. Much of the text on the public Internet is written by professional writers, who tend to write less badly.
When people use LLMs to generate text, they often ask it to write like a professional. (I haven't tried, but I assume that if you ask an LLM to write like a Reddit troll it will use a different set of forms.)
When you ask an LLM to write like a professional writer, it will aim to sound like a professional writer. They do in fact, and in speech, use words like "delve" and "robust" because they spend years cultivating their vocabularies.
Professional writers are comfortable with punctuation marks and know the difference between the em dash and the en dash, and when to use each versus other marks. (The typical non-professional cannot manage to use the apostrophe, much less the marks that require judgement.)
And a lot of them end up writing business content at some point in their careers. Which leads to an interesting mash where you may get "leverage" used as a verb alongside some of the other pattern tropes.
Because business writing is its own universe. LinkedIn has been swimming in content that would be flagged as LLM-generated for at least 10 years, long before ChatGPT landed.
I asked ChatGPT about that and it gave a nicely reasoned explanation on what AI produces compared to humans.
But that being said, the problem I think is that people treat the output from LLMs as final.
It should be treated more as idea generation or early draft to get over the “staring at a blank page” and get the creative juices flowing and creating your own content.
Having purely AI generated content and eventually feeding the algorithms and soon enough every sounds the same (already does in a lot of places).
Writing like this (say a technical blogpost) is supposed to communicate ideas effectively. Rhetoric, vocabulary, metaphors all aid this communication in good writing.
But the prompt is usually bereft of fully fleshed out ideas, so the LLM substitutes style in a futile attempt to amplify the signal.
Though maybe it’s not futile! HN voters eat this stuff up daily.
Generally, the more you write (and especially, the more you write long form content), the better your writing becomes. This also goes in reverse. Those who have great trouble writing, are unlikely to do much of it.
This alone can account for the seeming disparity. Though many people write poorly, they do not write much text for public consumption at all.
Most humans don’t, but maybe “most humans” do? As in, on average, as a collective, regressed to the mean of mediocrity and devoid of personality, we write like this? It’s not self-deprecating, it’s humbling.
Base models don't write like that. This appears during RLHF. It's not totally clear why*, but probably a large part of the answer is that this style looks great to human reviewers, and only starts looking terrible once you get to play around with the released model and realise it talks like that all the time.
* The technical term is "mode collapse", see [1][2]
[1] https://en.wikipedia.org/wiki/Mode_collapse
[2] https://gwern.net/doc/reinforcement-learning/preference-lear...
> This appears during RLHF. It's not totally clear why…
Imagine a world (ha) where everyone writing on LinkedIn from cafes and couches starts disrupting AI by opting into rating ChatGPT responses.
How might that turn out?
I suppose it might be because humans that use LLMs write like this.
Reading through this i feel like i'm on substack
They need to add “comprehensive tests” for Claude.
Bold-First Bullets, i do and like
Honestly, you need a tailored one of these for each of the major LLM model/version pairs. Claude and Gemini don't exhibit all of the same tropes in the same severities as OpenAI's GPT series, and within each of those, each revision sometimes exhibits substantial variance from the stylistic propensities of its immediate predecessor.
If only we could fix how it writes like garbage
Its so sad that perfectly fine patterns of writing are now associated with slop. Just because corporate greed couldnt stop themselves from making a bubble they then had to shove down our throats to prevent popping
Another popular one is ending headlines with a remark/alternative in parentheses. Especially "(why this matters)".
More generally, it's interesting that many different LLMs have differences in their favorite tropes but converge on broadly similar patterns. Of course ChatGPT and its default persona (you can choose others in the settings, but most people don't do that) is overrepresented in these examples. For example, the article doesn't mention the casual/based tone of Grok that often feels somewhat forced.
alternatively: "a guide for humans on to how to sound like LLMs"
This has sparked a discussion
Looks like this was a Show HN that didn't get much attention:
Show HN: Tropes.fyi – Name and shame AI writing - https://news.ycombinator.com/item?id=47088813 - Feb 2026 (3 comments)
I hope ossa-ma sees this second round!
Can we do this for everyone?
It's a bold strategy cotton. Bold of you to say that. Wild how mundane things get call wild. Thay're making calling things wild their entire personality. In that case, by your logic, (least generous misrepresentation of your logic).
One annoying trope I keep seeing in Gemini output is the punchy invented concept name in a tripartite list:
- “The Pledge”:…
- “The Turn”:…
- “The Prestige”:…
(For this particular example I used real terms from the stage magic world, at least according to Christopher Nolan’s film, as it captures the same meaningless-to-the-uninitiated quality.)
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?
Something that’s only peripherally interconnected is not a tapestry, but an LLM falling victim to these tropes will describe it as such for “punch”. Not just style with the absence of substance, but misleading substance.
> (let's play cat and mouse!).
No thanks, I hate this large scale social experiment
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Here what kimi thinks:
The post is moralizing theater masquerading as craft wisdom. “Just write it yourself” ignores the actual quality curve. Give a modern LLM a one-paragraph brief, ask for 600 words, then spend three minutes deleting the three most obvious adjectives and one “delve.” The result is already clearer, better structured, and more grammatically airtight than what 80 % of English-literate adults can produce in twenty distracted, coffee-spilled minutes. That isn’t speculation; it’s what every A/B test in every newsroom, ad agency, and SEO shop shows when copy is anonymized and editors pick winners. The average human twenty-minute draft loses—every single week.
I don’t care what an LLM thinks though. Care to share your own thoughts?
My reply has several layers, if it is not obvious - and one of the layers is my statement - can you guess what it is?
At the point it becomes clear a comment is written by AI it gets filed away as spam. I am not about to go crawling through the slop to find the nugget of stuff you wrote yourself. I do not care to take a guess. If you had a statement to share, then just do that.
At the point it becomes clear your comment is written by anti-AI bitter enthusiast.
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> ... But prose? That's from human to human, it's sacred and meant for other people. Using AI for that is deceitful.
I understand the sentiment. Meaning I think I understand some of the underlying frustration. But I don't care for the tone or the framing or the depth of analysis (for there isn't much there; I've seen the "if you didn't write it, why should I read it" cliché before *, and it ain't the only argument in town). Now for my detailed responses:
1. In the same way the author wants people to respect other people, I want the author to respect the complexity of the universe. I'm not seeing that.
2. If someone says "I wrote this without any LLM assistance" but do so anyway, THAT is clearly deceptive.
3. If you read a page that was created with LLM assistance, it isn't reasonable for you to say the creator was being deceptive just because you assumed. It takes two to achieve deception: both the sender and the receiver.
4. If you read a page on the internet, it is increasingly likely there was no human in the loop for the article at all. Good luck tracing the provenance of who made the call to make it happen. It might well be downstream of someone's job. (Yes, we can talk about diffusion of responsibility, etc., that's fair game -- but if you want to get into the realm of moral judgments, this isn't going to be a quick and tidy conversation)
5. I think the above comment puts too much of a "oh the halcyon days!" spin on this. Throughout history, many humans, much of the time, are largely repackaging things we had heard before. Unfortunately (or just "in reality") more of us are catching on to just how memetically-driven people are. We are both individuals and cogs. It is an uncomfortable truth. That brainwashed uncle you have is almost certainly a less reliable source of information than Claude.
6. The web has crappy incentives. It sucks. Yes, I want people to behave better. That would be nice, but I can't realistically expect people to behave better on the web unless there are incentives and consequences that align with what I want. The Web is a dumpster fire, not because of bad individuals, but because of system dynamics. Incentives. Feedback.
7. If people communicate more clearly, with fewer errors, that's at least a narrow win. One has to at least factor this in.
8. People accusing other people of being LLMs has a cost. Especially when people do it overconfidently or in a crude or mean manner. I've been on the receiving end. Why? Because I write in a way that sometimes triggers people because it resembles how LLMs write.
* I want to read high quality things. I actually care less if you wrote it as bullet points, with the help of an LLM, on a napkin, on a posterboard ... my goal is to learn from something suited to some purpose. I'm happy reading a computer-generated chart. I don't need a human to do that by hand.
The previous paragraph attempts to gesture at some of the conceptual holes in the common arguments behind "if you want a human to read it, a human should right it": they aren't systematically nor rigorously "wargamed" or "thought-experimented"; they are mostly just "knee-jerked".
I am quite interested in many things, including: (1) connecting with real people; (2) connecting with real people that don't merely regurgitate an information source they just ingested; (3) having an intelligent process generating the things I read. As an example of the third, I want "intelligent" organizations that synthesize contributions from their constituent parts. I want "intelligent" algorithms to help me focus on what matters to me. &c.
If a machine does that well, I'm not intrinsically bothered. If a human collaborates with an LLM to do that, fine. Whatever. We have bigger problems! Much bigger ones.
Yes, I want to live in a world where humans are valued for what they write and their intrinsic qualities, even as machines encroach on what used to be our biggest differentiator: intelligence itself. But wanting this and morally shaming people for not doing it doesn't seem like a good way to actually make it happen. Getting to that world, to my eye, requires public sense-making, grappling with the reality of how the world works, forming coalitions, organizing society, and passing laws.
Yes, I understand that HN has a policy that people write their own stuff, and I do. (See #8 above as well as my about page.)
Thank you to the approximately zero or maybe one person who made it this far. I owe you a beer. You can easily find me. I'm serious. But then we have to find a way to have a discussion while enjoying a beer on a video call. Alas.
I expect better from people -- and unfortunately a lot of people's output is lower quality than what I get from Claude. THIS is what pisses me off: that a machine-curated output is actually more useful to me than a vast majority of what people say, at least when I have particular questions to ask. This is one or many uncomfortable realities I would like people need to not flinch away from. As far as intelligent output is concerned, humans are losing a lot of ground. And fast. Don't shoot the messenger. If you don't recognize this, you might have a rather myopic view of intelligence that somehow assumes it must be biological or you just keep moving the goalposts. Or that somehow (but how?) humans "have it" but machines can't.
Agree.
I don't write for sentimentality. I write so that my code designs can survive longer than my work on it.
No documentation is worse than deceit.
The emptiness and vastness of the void (entropy) is much deeper than humans or machines.
Google search says this philosphy is called https://plato.stanford.edu/entries/content-externalism/
At the Ise Jingu, the shrine is not built to last; it is built to be reconstructed from scratch every twenty years.
If we want our systems to last, we would need the "process knowledge"—the actual mastery of the craft—to be in human hands rather than decaying in a dead system.
I don't think we can afford to process-knowledge-transfer many of our essential systems... without machine assistance.