Feature-Level Ensemble Learning for Robust Synthetic Text Detection with DeBERTaV3 and XLM-RoBERTa

Saman Sarker Joy, Tanusree Das Aishi

Abstract: As large language models, or LLMs, continue to advance in recent years, they require the development of a potent system to detect whether a text was created by a human or an LLM in order to prevent the unethical use of LLMs. To address this challenge, ALTA Shared Task 2023 introduced a task to build an automatic detection system that can discriminate between human-authored and synthetic text generated by LLMs. In this paper, we present our participation in this task where we proposed a feature-level ensemble of two transformer models namely DeBERTaV3 and XLM-RoBERTa to come up with a robust system. The given dataset consisted of textual data with two labels where the task was binary classification. Experimental results show that our proposed method achieved competitive performance among the participants. We believe this solution would make an impact and provide a feasible solution for detection of synthetic text detection.