arxiv:2510.20610

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

Published on Oct 23

Authors:

Ali Zain ,

Abstract

Multilingual XLM-RoBERTa outperformed specialized Arabic models in AI-generated text detection with an F1 score of 0.7701.

AI-generated summary

This paper details our submission to the Ara- GenEval Shared Task on Arabic AI-generated text detection, where our team, BUSTED, se- cured 5th place. We investigated the effec- tiveness of three pre-trained transformer mod- els: 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 sur- prising result: the multilingual XLM-RoBERTa model achieved the highest performance with an F1 score of 0.7701, outperforming the spe- cialized Arabic models. This work underscores the complexities of AI-generated text detection and highlights the strong generalization capa- bilities of multilingual models.

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