The recent proliferation of AI-generated content has prompted significant interest in developing reliable detection methods. This study explores techniques for identifying AI-generated text through sentence-level evaluation within hybrid articles. Our findings indicate that ChatGPT-3.5 Turbo exhibits distinct, repetitive probability patterns that enable consistent in-domain detection. Empirical tests show that minor textual modifications, such as rewording, have minimal impact on detection accuracy. These results provide valuable insights for advancing AI detection methodologies, offering a pathway toward robust solutions to address the complexities of synthetic text identification.
@article{arxiv.2412.19076,
title = {Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis},
author = {Dima Galat},
journal= {arXiv preprint arXiv:2412.19076},
year = {2024}
}