Unfortunately many believe they can, and it is impossible to disprove. So now real people need to write avoiding certain styles, because a lot of other people have decided those are "LLM clues." Bullets, EM Dash, certain common English phases or words (e.g. Delve, Vibrant, Additionally, etc)[0].
Basicaly you need to sprinkle subtle mistakes, or lower the quality of your written communications to avoid accusations that will side-track whatever youre writing into a "you're a witch" argument. Ironically LLM accusations are now a sign of the high quality written word.
Someone with native fluency in American English can (should) be able to tell the difference between human writing and unpolished AI copy-paste.
Essentially 0 people use emoji to create a bulleted list. Nobody unintentionally cites fake legal precedents or non-existent events, articles, or papers. Even the “it’s not X, it’s Y” structure, in the presence of other suspicious style/tone cues signals LLM text.
I'm going to ask the qustion I ask everyone who makes the claim that they wrote like that for years: Can you show us a link from prior 2022 that you wrote like that?
Sure, but, look, we have seen these claims so many times, that if it were true by now someone would have linked at least one archived blog post to show that it is, indeed, how humans used to write.
I think that’s a RLHF issue - if you ask people “which looks better”, they too-frequently picked the emoji list. Same with the overuse of bolding. I think it’s also why the more consumer-facing models are so fawning: people like to be praised.
So are you saying that anyone with native fluency in English but who is not from the US can't tell the difference between human writing and unpolished AI copy-paste?
I don't agree.
Given that US-based LLM models tend to default their output to American English, its arguably much easier for "the rest of us" to spot the "US" language patterns...
> Unfortunately many believe they can, and it is impossible to disprove. So now real people need to write avoiding certain styles, because a lot of other people have decided those are "LLM clues." Bullets, EM Dash, certain common English phases or words (e.g. Delve, Vibrant, Additionally, etc)[0].
I think people will be able to detect the lowest-user-effort version of LLM text pretty reliably after a while (ie what you describe; many people have a good sense of LLM clues). But there's probably a *ton* of LLM text out there where some of the instructions given were "throw a few errors in", "don't use bullet points or em dashes", "don't do the it's not this, it's that thing" going undetected.
And then those changes will get built into ChatGPT's main instructions, and in a few months people will start to pick up on other indicators, and then slightly smarter/more motivated users will give new instructions to hide their LLM usage... (or everyone stops caring, which is an outcome I find hard to wrap my head around)
This is the correct answer. We’re at a point where it will soon be safer to assume a human or someone with agency and their approval wrote the text, than to completely dismiss it as “written by LLM” or a human.
So judge the content on its merit irrespective of its source.
Indeed, isomorphic plagiarism by its nature forms strong vector search paths that were made from stealing both global websites, real peoples work, and LLM user-base input/markdown.
However, reasoning models adding a random typo to seem less automated, still do not hide the fairly repeatable quantized artifacts from the training process. For LLM, it is rather trivial to find where people originally scraped the data from if they still have annotated training metadata.
Finally, reading LLM output is usually clear once one abandons the trap of thinking "I think the author meant [this/that]", and recognizing a works tone reads like a fake author had a stroke [0]. =3
And I'm sure we've all seen what happens if you run the Declaration of Independence or the Gettysburg Address or the book of Genesis through an AI "detector". They usually come back as AI.
> Ironically LLM accusations are now a sign of the high quality written word.
Citation needed. The LLM accusations come from the specific cadence they use. You can remove all em-dashes from a piece of text and it still becomes clear when something is LLM written.
Can they be prompted to be less obvious? Sure, but hardly anyone does that.
It's more "The Core Insight", "The Key Takeaway", etc. than it is about emdashes.
Incidentally, the only people annoyed about "witch-hunts" tend to be those who are unable to recognise cadence in the written word.
I don't think you can 100% detect AI content, because at some point someone will just prompt the AI to not sound like AI.
I think the better question to ask is: What are your goals? Is it to prevent AI SPAM, or to discourage people copy-pasting AI? Those are two very different problems: in the case of AI SPAM you look for patterns of usage, (IE, unusually high interaction from a single IP, timing patterns around when things are read and the response comes in,) and in the other case it all comes down to cultural norms.
For HN comments, the LLMs seem to really like 2 or 3 paragraphs long responses. It's pretty obvious when you click a profile's comments and see every comment being that exact same structure.
You can try to use an ai detector, here is a leaderboard of the best ones according to this benchmark: https://raid-bench.xyz/leaderboard
Results should of course always be taken with a grain of salt, but in most cases detectors are quite good in my opinion.
Pangram is probably the best known example of a detector with low false positives, they have a research paper here: https://arxiv.org/pdf/2402.14873. They do have an API but not sure if you need to request access for it.
For humans I think it just comes down to interacting with LLMs enough to realize their quirks, but that's not really fool-proof.
I don't look at whether the text is written by an LLM but at whether it has substance and whether the writer understands what they are doing and is respecting my time.
If the text is full of punchy three word phrases or nonsense GenAI images then that's an obvious sign. But so is if the other person has some revolutionary project with great results but they can't really explain why their solution works where presumably many failed in the past (or it's a word salad, or some lengthy writing that doesn't show any signs of getting you to an "aha, that's some great insight" moment).
A good sign is also if the author had something interesting going before 2022, and they didn't fall into the earliest low quality LLM waves. Unfortunately some genuinely talented people have started using LLMs to turbocharge their output while leaving some quality on the table nowadays, so I don't really know. I'm becoming a lot more sceptical of the Internet, to be honest.
It's a lot easier to detect when you mostly interact with non English speakers.
I asked an LLM to rewrite this to make it nicer and got the following. I'd flag the first because I don't usually hear "majority of your interactions" in conversation but I might miss it. The second will probably get by me. As for the third, I never say "considerably easier" unless I'm trying to sound artificially posh.
1. It becomes much more noticeable when the majority of your interactions are with non-native English speakers.
2.It tends to stand out more when most of the people you interact with speak English as a second language.
3. It's considerably easier to identify when most of your interactions involve people whose primary language isn't English.
Overuse of "it's not X, it's Y" kind of writing, strange shifts in writing or thinking patterns, and excessive formatting (or, when I'm on wikipedia especially, ineffective formatting (such as using MD where it isn't supported))
Instead of trying to detect AI in the final string of text the industry seems to be moving towards proof of process. version history, drafts protocol or even UI level logging of keystrokes. If i can not prove i spent three hours in a doc via a series of incremental diffs, the humanness of my prose becomes irrelevant in a high stakes environment. Detection is a lagging indicator the only leading indicator is the audit trail of the labor itself.
There are some systems which can use the LLMs themselves to detect writing (basically, if the text matches what the LLM would predict too well, it's probably LLM generated), but they are far from infallible (with both false positives and false negatives). There's also certain tropes and quirks which LLMs tend to over-use which can be fairly obvious tells but they can be suppressed and they do represent how some people actually write.
Humans detect them mostly through pattern matching. However, for systems, my guess is that a ML model is trained on AI genres texts to detect AI generated texts.
I bet you a paycheck that of anyone read one of the “97 things” books, they would think the essays are AI generated even though they came out way before LLMs.
Specific language tells, such as: unusual punctuation, including em–dashes and semicolons; hedged, safe statements, but not always; and text that showcases certain words such as “delve”.
Here’s the kicker. If you happen to include any of these words or symbols in your post they’ll stop reading and simply comment “AI slop”. This adds even less to the conversation than the parent, who may well be using an LLM to correct their second or third language and have a valid point to make.
62 comments
Unfortunately many believe they can, and it is impossible to disprove. So now real people need to write avoiding certain styles, because a lot of other people have decided those are "LLM clues." Bullets, EM Dash, certain common English phases or words (e.g. Delve, Vibrant, Additionally, etc)[0].
Basicaly you need to sprinkle subtle mistakes, or lower the quality of your written communications to avoid accusations that will side-track whatever youre writing into a "you're a witch" argument. Ironically LLM accusations are now a sign of the high quality written word.
[0] https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
Essentially 0 people use emoji to create a bulleted list. Nobody unintentionally cites fake legal precedents or non-existent events, articles, or papers. Even the “it’s not X, it’s Y” structure, in the presence of other suspicious style/tone cues signals LLM text.
Ask an LLM to read your project specs and add a section headed: Performance Optimizations, to see an example of this
Another is a certain punchy and sensationalist style that does not change throughout a longer piece of writing.
Eg - The Strait of Hormuz: Chokepoint or Opportunity?
I suppose my high school essays were not. Apologies, but those are lost.
The lack of a single example is very telling.
>Even the “it’s not X, it’s Y” structure
I wonder where some of this comes from. Another one is 'real unlock', it's not a common phrasing that I really recall.
https://trends.google.com/explore?q=real%2520unlock&date=all...
> 0 people use emoji to create a bulleted list.
I haven't seen this yet, but I guess the only reason I haven't done it is because it never crossed my mind.
What I have found an easy detection is non-breaking spaces. They tend to get littered through the passages of text without reason.
> It’s the fake drama. Punchy sentences. Contrast. And then? A banal payoff.
It's great because it's a double-decker of annoying marketing copy style and nonsensical content.
[0]: https://news.ycombinator.com/item?id=47615075
> Unfortunately many believe they can, and it is impossible to disprove. So now real people need to write avoiding certain styles, because a lot of other people have decided those are "LLM clues." Bullets, EM Dash, certain common English phases or words (e.g. Delve, Vibrant, Additionally, etc)[0].
I think people will be able to detect the lowest-user-effort version of LLM text pretty reliably after a while (ie what you describe; many people have a good sense of LLM clues). But there's probably a *ton* of LLM text out there where some of the instructions given were "throw a few errors in", "don't use bullet points or em dashes", "don't do the
it's not this, it's thatthing" going undetected.And then those changes will get built into ChatGPT's main instructions, and in a few months people will start to pick up on other indicators, and then slightly smarter/more motivated users will give new instructions to hide their LLM usage... (or everyone stops caring, which is an outcome I find hard to wrap my head around)
So judge the content on its merit irrespective of its source.
However, reasoning models adding a random typo to seem less automated, still do not hide the fairly repeatable quantized artifacts from the training process. For LLM, it is rather trivial to find where people originally scraped the data from if they still have annotated training metadata.
Finally, reading LLM output is usually clear once one abandons the trap of thinking "I think the author meant [this/that]", and recognizing a works tone reads like a fake author had a stroke [0]. =3
[0] https://en.wikipedia.org/wiki/Stroke
> Ironically LLM accusations are now a sign of the high quality written word.
Citation needed. The LLM accusations come from the specific cadence they use. You can remove all em-dashes from a piece of text and it still becomes clear when something is LLM written.
Can they be prompted to be less obvious? Sure, but hardly anyone does that.
It's more "The Core Insight", "The Key Takeaway", etc. than it is about emdashes.
Incidentally, the only people annoyed about "witch-hunts" tend to be those who are unable to recognise cadence in the written word.
As far as how I / other people do it, there are some obvious styles that reek of LLMs, I think it’s chatgpt.
There’s a very common structure of “nice post, the X to Y is real. miscellaneous praise — blah blah blah. Also curious about how you asjkldfljaksd?"
From today:
This comment is almost certainly AI-generated: https://news.ycombinator.com/item?id=47658796
And I'm suspicious of this one too - https://news.ycombinator.com/item?id=47660070 - reads just a bit too glazebot-9000 to believe it's written by a person.
I think the better question to ask is: What are your goals? Is it to prevent AI SPAM, or to discourage people copy-pasting AI? Those are two very different problems: in the case of AI SPAM you look for patterns of usage, (IE, unusually high interaction from a single IP, timing patterns around when things are read and the response comes in,) and in the other case it all comes down to cultural norms.
This is an artifact of the default LLM writing style, cross-poisoned through training on outputs -- not an "universal" property.
For humans I think it just comes down to interacting with LLMs enough to realize their quirks, but that's not really fool-proof.
If the text is full of punchy three word phrases or nonsense GenAI images then that's an obvious sign. But so is if the other person has some revolutionary project with great results but they can't really explain why their solution works where presumably many failed in the past (or it's a word salad, or some lengthy writing that doesn't show any signs of getting you to an "aha, that's some great insight" moment).
A good sign is also if the author had something interesting going before 2022, and they didn't fall into the earliest low quality LLM waves. Unfortunately some genuinely talented people have started using LLMs to turbocharge their output while leaving some quality on the table nowadays, so I don't really know. I'm becoming a lot more sceptical of the Internet, to be honest.
I asked an LLM to rewrite this to make it nicer and got the following. I'd flag the first because I don't usually hear "majority of your interactions" in conversation but I might miss it. The second will probably get by me. As for the third, I never say "considerably easier" unless I'm trying to sound artificially posh.
1. It becomes much more noticeable when the majority of your interactions are with non-native English speakers.
2.It tends to stand out more when most of the people you interact with speak English as a second language.
3. It's considerably easier to identify when most of your interactions involve people whose primary language isn't English.
Note that on Kagi, you can click "Report this page as AI-generated" [1]. Unfortunately though, my last report from January is still "under review" :/
[1] https://help.kagi.com/kagi/features/slopstop.html
To me, it often feels like the text version of the uncanny valley.
But again, that's just "feels", I don't have proof or anything.
https://github.com/97-things/97-things-every-programmer-shou...
There are a couple of tells like em dashes and similar patterns but you should be able to suppress that with even a simple prompt.
Specific language tells, such as: unusual punctuation, including em–dashes and semicolons; hedged, safe statements, but not always; and text that showcases certain words such as “delve”.
Here’s the kicker. If you happen to include any of these words or symbols in your post they’ll stop reading and simply comment “AI slop”. This adds even less to the conversation than the parent, who may well be using an LLM to correct their second or third language and have a valid point to make.