Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT
원문 초록
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.
함께 살펴볼 논문
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Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models
Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack
Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning
RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
SUCEA: Reasoning-Intensive Retrieval for Adversarial Fact-checking through Claim Decomposition and Editing
토론
이 논문과 연결된 포럼 글이 아직 없습니다.