This paper describes the solutions submitted by the UPB team to the
AuTexTification shared task, featured as part of IberLEF-2023. Our team
participated in the first subtask, identifying text documents produced by large
language models instead of humans. The organizers provided a bilingual dataset
for this subtask, comprising English and Spanish texts covering multiple
domains, such as legal texts, social media posts, and how-to articles. We
experimented mostly with deep learning models based on Transformers, as well as
training techniques such as multi-task learning and virtual adversarial
training to obtain better results. We submitted three runs, two of which
consisted of ensemble models. Our best-performing model achieved macro
F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset.

该研究论文描述了 UPB 团队在 IberLEF-2023 的 AuTexTification 共享任务中提交的解决方案，主要使用基于 Transformer 的深度学习模型以及多任务学习等训练技术，最佳模型在英语和西班牙语数据集上分别达到了 66.63% 和 67.10% 的宏 F1 分数。