Machine generated text detection by fine tuning of language models === 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 " (see here) written by Nicolai Thorer Sivesind and Andreas Bentzen Winje at the Department of Computer Science at the Norwegian University of Science and Technology . 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 were 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). The dataset card for the dataset that was created in relation to this project can be found here. NOTE : the hosted inference on this site only works for the RoBERTa models, and not for the Bloomz models. The Bloomz models otherwise can produce wrong predictions when not explicitly providing the attention mask from the tokenizer to the model for inference. To be sure, the pipeline library seems to produce the most consistent results…
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