This paper describes the approach of the UniBuc - NLP team in tackling the SemEval 2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. We explored transformer-based and hybrid deep learning architectures. For subtask B, our transformer-based model achieved a strong \textbf{second-place} out of 77 teams with an accuracy of \textbf{86.95\%}, demonstrating the architecture's suitability for this task. However, our models showed overfitting in subtask A which could potentially be fixed with less fine-tunning and increasing maximum sequence length. For subtask C (token-level classification), our hybrid model overfit during training, hindering its ability to detect transitions between human and machine-generated text.
@article{arxiv.2405.17964,
title = {Transformer and Hybrid Deep Learning Based Models for Machine-Generated Text Detection},
author = {Teodor-George Marchitan and Claudiu Creanga and Liviu P. Dinu},
journal= {arXiv preprint arXiv:2405.17964},
year = {2024}
}