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People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text

Jenna Russell, Marzena Karpinska, Mohit Iyyer

2025 · arXiv

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From the abstract

In this paper, we study how well humans can detect text generated by commercial LLMs (GPT-4o, Claude, o1). We hire annotators to read 300 non-fiction English articles, label them as either human-written or AI-generated, and provide paragraph-length explanations for their decisions. Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such "expert" annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. Qualitative analysis of the experts' free-form explanations shows that while they rely heavily on specific lexical clues ('AI vocabulary'), they also pick up on more complex phenomena within the text (e.g., formality, originality, clarity) that are challenging to assess for automatic detectors. We release our annotated dataset and code to spur future research into both human and automated detection of AI-generated text.

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Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels

13 selected excerpts · 13 total mentions

neom
Neutral mention · rule
Most people do not realize when a personal message they receive was written by AI, study finds - https://theconversation.com/most-people-do-not-realize-when-... People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text - https://arxiv.org/pdf/2501.15654
Seattle3503
Neutral mention · rule
Do you have evidence Pangram is unreliable? There are independent evaluations [1, 2] showing it works, and it's getting used more and more scientific papers. Have you used it or evaluated it yourself? What do you think these other evaluators are doing or getting wrong? 1: https://bfi.uchicago.edu/insights/artificial-writing-and-aut... 2: https://arxiv.org/pdf/2501.15654
chis
Neutral mention · rule
This has been pretty well studied. Pangram is about as good at an expert human with a 98% detection rate and <2% false positive, for flagging AI generated text. https://arxiv.org/pdf/2501.15654
embedding-shape
Neutral mention · rule
Humans writing more like LLMs, just like new LLMs write more like humans, it's all coalescing into one. I've copied-pasted comments I made on HN from like 2020 and had it tell me it's "100% AI". I've seen examples where the services claim "100% AI" because there was no normal dashes, only em-dashes. Even have a recent example from HN itself: https://news.ycombinator.com/item?id=48165690 > This reads very AI. Pangram [0] agrees [1]. [0] Not perfect, but I think as good evidence as any: https://arxiv.org/pdf/2501.15654 [1] https://www.pangram.com/history/44cd07d3-ba94-4331-8c7f-a626... Said Pangram report literally citing the single evidence of em-dashes...
Groxx
Neutral mention · rule
Rather obviously they're choosing the one that makes them look best. Another they link to¹ shows 98% for example. Much more importantly, 9/10 dentists agree it's the best. 1: https://arxiv.org/pdf/2501.15654, linked from² https://www.pangram.com/blog/third-party-pangram-evals (the second section) 2: the third study they link there is based entirely around the assumption that Pangram is correct, and seems to have been a collaboration or something as they're included in the credits area.
nothinkjustai
Neutral mention · rule
There is research showing the contrary that is far more convincing: > Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such “expert” annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. https://arxiv.org/html/2501.15654v2
samdoesnothing
Neutral mention · rule
I don't think people should be obligated to spend time and effort justifying their reasoning on this. Firstly it's highly asymmetrical; you can generate AI content with little effort, whereas composing a detailed analysis requires a lot more work. It's also not easily articulatable. However there is evidence that writers who have experience using LLMs are highly accurate at detecting AI generated text. > Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such “expert” annotators misclassifies only 1 of 300 articles,…
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significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. Qualitative analysis of the experts’ free-form explanations shows that while they rely heavily on specific lexical clues, they also pick up on more complex phenomena within the text that are challenging to assess for automatic detectors. [0] Like the paper says, it's easy to point to specific clues in ai generated text, like the overuse of em dashes, overuse of inline lists, unusual emoji usage, tile case, frequent use of specific vocab, the rule of three, negative parallelisms, elegant variation, false ranges etc. But harder to articulate and perhaps more important to recognition is overall flow, sentence structure and length, and various stylistic choices that scream AI. Also worth noting that the author never actually stated that they did not use generative AI for this article. Saying that their hands were on the keyboard or that they reworked sentences and got feedback from coworkers doesn't mean AI wasn't used. That they haven't straight up said "No AI was used to write this article" is another indication. 0: https://arxiv.org/html/2501.15654v2
Jweb_Guru
Neutral mention · rule
Human experts can reliably detect some kinds of long-form, AI-generated text using exactly the same sorts of cues I've outlined: https://arxiv.org/html/2501.15654v1. You may take issue with the quality of the paper, but there have been very few studies like this and this one found an extremely strong effect. I am making an even more limited claim than the article, which is only that it's possible for "experts" (i.e. people who frequently interact with LLMs as part of their day jobs) to identify AI generated text in long-form passages in a way that has very few false positives, not classify it perfectly. I've also introduced the caveat that this only applies to AI…
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generated text that has received minimal or no prompting to "humanize" the writing style, not AI generated text in general. If you would like to perform a higher-quality study with more recent models, feel free (it's only fair that I ask you to do an unreasonable amount of work here given that your argument appears to be that if I don't quit my lucrative programming job and go manually classify text for pennies on the dollar, it proves that it can't be done). The reason this isn't offered as a service is because it makes no economic sense to do so using humans, not because it's impossible as you claim. This kind of "human" detection mechanism does not scale the way generation does. The cues that I rely on are also pretty easy to eliminate if you know someone is looking for them. This means that heuristics do not work reliably against someone actively trying to avoid human detection, or a human deliberately trying to sound like an LLM (I feel the need to reiterate this as many of the counterarguments to what I'm saying are to claims of this form). > I’m not going to write another detailed explanation of why your “slop === AI” premise is flawed. This isn't a claim that I made. I believe that text written with LLM assistance is not necessarily slop, and that slop is not necessarily AI generated. The only assertion I made regarding slop is that being written with LLM assistance with minimal prompting or editing is a strong predictor of slop, and that the heuristics I'm using (if present in large quantities) are a strong predictor of an article being written with LLM assistance with minimal prompting or editing. i.e. I, I am asserting that these kinds of heuristics work pretty well on articles generated by people who don't realize (or care) that there are LLM "tells" all over their work. The fact that many of the articles posted to HN are being accused of being LLM generated could certainly indicate that this is all just a massive witch hunt, but given the acknowledged popularity of ChatGPT among the general population and the fact that experts can pretty easily identify non-humanized articles, I think "a lot of people are using LLMs in the process of generating their blog posts, and some sizable fraction of those people didn't edit the output very much" is an equally compelling hypothesis.
Chu4eeno
Neutral mention · rule
It's also confirmed in third-party studies like this https://arxiv.org/html/2501.15654v1 (which also confirms that humans that have experience with LLMs are very good at spotting them).

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