Abstract
Improved fluency in large language models has intensified the need for accurate detection of machine-generated text.This study evaluates transformer-based models using an improved version of the Conference on Computational Linguistics 2025 (COLING 2025), Generative Artificial Intelligence (GenAI) Content Detection Task 1 dataset, which was carefully preprocessed to enhance label quality and balance.All models were trained under a unified protocol to ensure fair comparison and robust evaluation.Test set results show that Decoding-Enhanced Bert with Disentangled Attention (DeBERTa) achieves the highest macro F1-Score of 85.48%, surpassing the previously top-ranked Multi-Task Learning (MTL) system, which attains a macro F1 of 83.07%.These results highlight the effectiveness of advanced transformer architectures for distinguishing human-written and machine-generated text.Despite these gains, performance degradation under domain shift and highly paraphrased inputs remains a challenge.