Technical Report on the Pangram AI-Generated Text Classifier · HackerPapers discussions

Technical Report on the Pangram AI-Generated Text Classifier — mentioned by WhitneyLand

Thu, 16 Jul 2026 19:54:55 GMT

"Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it...it's a bad fiction to perpetuate that any of this is anything more than tarot card reading." Not true at all. Pangram is highly effective and has a very low false positive rate. The post here is impressive for a small project, it looks like they independently thought of one of the core ideas Pangram uses of creating twins to compare. You can see how it works here: https://arxiv.org/pdf/2402.14873

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Technical Report on the Pangram AI-Generated Text Classifier — HN submission

Mon, 06 Jul 2026 02:27:50 GMT

Technical Report on the Pangram AI-Generated Text Classifier (2024)

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Technical Report on the Pangram AI-Generated Text Classifier — mentioned by salahadawi

Sun, 05 Jul 2026 07:26:13 GMT

Their technical report (arxiv.org/abs/2402.14873) goes into it. They train a classifier on human text paired with AI-generated versions of the same documents. Dataset scale is comparable to LLM pretraining sets.

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Technical Report on the Pangram AI-Generated Text Classifier — mentioned by moonu

Mon, 06 Apr 2026 12:40:31 GMT

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.

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Technical Report on the Pangram AI-Generated Text Classifier — mentioned by usefulposter

Tue, 10 Feb 2026 19:20:51 GMT

I'm not affiliated with it in any way, but you might want to check out the leading model https://arxiv.org/abs/2402.14873 and the mixed authorship detection https://www.pangram.com/blog/pangram-3-0-technical. LLM detection has come a long way since the first GPTZero. Human heuristics - I've prompted millions of tokens across every frontier model iteration for all manner of writing styles and purposes - also helps greatly. Concerning to me are long-time posters who (perhaps unknowingly) advance the decline of this human community by encouraging the people breaking HN guidelines. Perhaps…

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Technical Report on the Pangram AI-Generated Text Classifier — mentioned by EdwardDiego

Sat, 31 Jan 2026 22:45:42 GMT

Human editors, I suspect, would pick up the "tells" of generated text, although as we know, there's a lot of false positives in that space. But it looks like Pangram is a text classifying NN trained using a technique where they get a human to write a body of text on a subject, and then get various LLMs to write a body of text on the same subject, which strikes me as a good way to approach the problem. Not that I'm in anyway qualified to properly understand ML. More details here: https://arxiv.org/pdf/2402.14873

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Technical Report on the Pangram AI-Generated Text Classifier — mentioned by gamegoblin

Thu, 29 Jan 2026 03:52:17 GMT

AI detectors in general are unreliable, but there are a few made by serious researchers that have only 1-in-10000 false positive rate, e.g. https://arxiv.org/pdf/2402.14873 Having worked in a bigcorp, I've read my fair share of management-speak, and none of it sounds quite as empty as the allegedly AI text. The AI sounds like someone conjuring a parody emulation of management speak instead of actual management speak. More broadly — and I feel this way about AI code at well as AI prose — I find that part of my brain is always trying to reverse engineer what kind of person wrote this, what was…

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Technical Report on the Pangram AI-Generated Text Classifier — mentioned by agucova

Mon, 17 Nov 2025 04:34:31 GMT

I ran the introduction chapter through Pangram [1], which is one of the most reliable AI-generated text classifiers out there [2] (with a benchmarked accuracy of 99.85% over long-form text), and it gives high confidence for it having been AI-generated. It's also very intuitively obvious if you play a lot with LLMs. I have no problem at all reading AI-generated content if it's good, but I don't appreciate dishonesty. [1]: https://www.pangram.com/ [2]: https://arxiv.org/pdf/2402.14873

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Technical Report on the Pangram AI-Generated Text Classifier — mentioned by maxspero

Mon, 28 Oct 2024 22:10:02 GMT

We benchmark on pre-2023 datasets of O(10M) documents not in our training set. Other detectors seem to have between 1-3% false positive rate and ours is around 1 in 10,000 as of our latest model update. We do a lot of active learning + core set selection to keep FPR low and improve recall on larger LLMs. Our white paper with some methodology is here: https://arxiv.org/abs/2402.14873

Paper and discussion history. A mention is not an endorsement.

Technical Report on the Pangram AI-Generated Text Classifier — HN submission

Fri, 01 Mar 2024 18:28:47 GMT

Technical Report on the Checkfor.ai AI-Generated Text Classifier

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.