Work

BUSTED at ARATECT Shared Task: A Comparative Study of Transformer-Based Models for Arabic AI-Generated Text Detection

Year: 2025

Type: conference-paper

Abstract: This paper details our submission to the Ara-GenEval Shared Task on Arabic AI-generated text detection, where our team, BUSTED, secured 5th place.We investigated the effectiveness of three pre-trained transformer models: AraELECTRA, CAMeLBERT, and XLM-RoBERTa.Our approach involved fine-tuning each model on the provided dataset for a binary classification task.Our findings revealed a surprising outcome: the multilingual XLM-RoBERTa model achieved the highest performance with an F1-score of 0.7701, outperforming the specialized Arabic models.This work underscores the complexities of AI-generated text detection and highlights the strong generalization capabilities of multilingual models.

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.18653/v1/2025.arabicnlp-sharedtasks.12

Authors: Ali Zain, Sareem Farooqui, Muhammad Rafi

Institution: National University of Computer and Emerging Sciences

Language: English

Topic: Authorship Attribution and Profiling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National University of Computer and Emerging Sciences

OpenAlex ID: W4416037828