Synthetic Text

Synthetic text detection focuses on identifying text generated by artificial intelligence, primarily large language models, to maintain academic integrity and combat misinformation. Current research emphasizes developing robust detectors using techniques like contrastive learning, representation learning, and emotion analysis to distinguish synthetic text from human-written content, often leveraging Siamese networks or other novel architectures. The ability to reliably detect synthetic text is crucial for maintaining trust in online information, ensuring academic rigor, and addressing the ethical implications of increasingly sophisticated AI-generated content.