Human involvement in AI-generated academic text can be estimated continuously by training a RoBERTa regressor on BERTScore-derived labels, outperforming binary detectors on a new synthetic dataset.
Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT
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abstract
The abundance of information on social media has increased the necessity of accurate real-time rumour detection. Manual techniques of identifying and verifying fake news generated by AI tools are impracticable and time-consuming given the enormous volume of information generated every day. This has sparked an increase in interest in creating automated systems to find fake news on the Internet. The studies in this research demonstrate that the BERT and RobertA models with fine-tuning had the best success in detecting AI generated news. With a score of 98%, tweaked RobertA in particular showed excellent precision. In conclusion, this study has shown that neural networks can be used to identify bogus news AI generation news created by ChatGPT. The RobertA and BERT models' excellent performance indicates that these models can play a critical role in the fight against misinformation.
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Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing
Human involvement in AI-generated academic text can be estimated continuously by training a RoBERTa regressor on BERTScore-derived labels, outperforming binary detectors on a new synthetic dataset.