Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text
This work addresses the challenge of AI-generated text detection for natural language processing applications, but it is incremental as it builds on existing methods without introducing a new paradigm.
The paper tackled the problem of detecting AI-generated text in monolingual and multilingual settings by evaluating various methods, including deep-learning, MPU, fine-tuning, and ensemble techniques, achieving 8th place in accuracy on the official test set for a multilingual subtask.
This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and accuracy. In the monolingual task, we evaluated traditional deep-learning methods, multiscale positive-unlabeled framework (MPU), fine-tuning, adapters and ensemble methods. Then, we selected the top-performing models based on their accuracy from the monolingual models and evaluated them in subtasks A and B. The final model construction employed a stacking ensemble that combined fine-tuning with MPU. Our system achieved 8th (scored 8th in terms of accuracy, officially ranked 13th) place in the official test set in multilingual settings of subtask A. We release our system code at:https://github.com/warmth27/SemEval2024_QUST