Team Unibuc - NLP at SemEval-2024 Task 8: Transformer and Hybrid Deep Learning Based Models for Machine-Generated Text Detection
Teodor-George Marchitan, Claudiu Creanga, Liviu P. Dinu · 2024
This paper describes the approach of the UniBuc -NLP team in tackling the Se-mEval 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 transformerbased model achieved a strong second-place out of 77 teams with an accuracy of 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.