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Detecting GPT-2 Generations with DetectGPT

This web app is a demo of DetectGPT, described in this paper. DetectGPT is a general-purpose method for using a language model to detect its own generations; however, this proof-of-concept only detects if a particular piece of text came from GPT-2. Detections on samples from other models may be particularly unreliable. We may add larger models like GPT-J (6B), GPT-NeoX (20B), or GPT-3 (175B) in the future; we perform evaluations with these and other models in our paper. The official implementation of DetectGPT can be found here.

This demo currently does not support languages using non-Latin script. Sorry for the inconvenience; we're hoping to add support soon!

[Update 7 Mar 2023] Due to high traffic, we have begun caching requests locally. Please do not submit sensitive or private information to this demo.


Enter some text in the text box at the bottom of the page and click the "Detect" button. You can try the example texts in the table below to get started, or use the generation box to generate your own text from GPT-2. We'd love to hear your thoughts (whether successes or failures) on DetectGPT at [email protected]!

This demo is experimental; its predictions should not be used to justify real-world decisions.

Example Texts

Maryland's environmental protection agency is suing a [...]
Taylor Guitars is an American guitar manufacturer based [...]
When I was young, my mother would often tell me stories [...]
The World Health Organization will push at [...]
Nature comprises many processes that recycle various [...]

(Optional) Generate Your Own GPT-2 Text for Testing

Try Out DetectGPT

Human texts on this page come from this WaPo article, this Wikipedia article, the top-rated response to this /r/WritingPrompts post by user OrcDovahkiin, this Reuters article, and this essay from EduBirdie on the water cycle. GPT-2 outputs are generated by prompting GPT-2 a short prefix of each human sample (or your prompt) and sampling up to 200 tokens with temperature 1.0.

This web app is a demo of the DetectGPT method described in this paper. We can't make any guarantees about the accuracy of the results, but we hope you find it interesting! We are very grateful for the Ray distributed compute framework for making this web app much, much easier to build.

Privacy notice: this web app does not collect any personal information beyond the text you submit for detection, which is cached for performance reasons.