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Machine-generated text-detection by fine-tuning of language models
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This project is related to a bachelor's thesis written by Nicolai Thorer Sivesind and Andreas Bentzen Winje at the *Department of Computer Science* at the *Norwegian University of Science and Technology*.
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It contains text classification models trained to distinguish human-written text from text generated by language models like ChatGPT and GPT-3. The best models was able to achieve an accuracy of 100% on real and *GPT-3*-generated wikipedia articles (4500 samples), and an accuracy of 98.4% on real and *ChatGPT*-generated research abstracts (3000 samples).
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Machine-generated text-detection by fine-tuning of language models
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This project is related to a bachelor's thesis with the title "*Turning Poachers into Gamekeepers: Detecting Machine-Generated Text in Academia using Large Language Models*" (not yet published) written by *Nicolai Thorer Sivesind* and *Andreas Bentzen Winje* at the *Department of Computer Science* at the *Norwegian University of Science and Technology*.
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It contains text classification models trained to distinguish human-written text from text generated by language models like ChatGPT and GPT-3. The best models was able to achieve an accuracy of 100% on real and *GPT-3*-generated wikipedia articles (4500 samples), and an accuracy of 98.4% on real and *ChatGPT*-generated research abstracts (3000 samples).
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