@inproceedings{labib-etal-2026-reglat,
    title = "{REGLAT} at {A}bjad{G}en{E}val: Multi-Model Ensemble Approach for {A}rabic {AI}-Generated Text Detection",
    author = "Labib, Mariam  and
      Ashraf, Nsrin  and
      Fetouh, Ahmed M.  and
      Nayel, Hamada",
    editor = "El-Haj, Mo  and
      Rayson, Paul  and
      Jarrar, Mustafa  and
      Ezeani, Ignatius  and
      Ezzini, Saad  and
      Ahmadi, Sina  and
      Haddad Haddad, Amal  and
      Amol, Cynthia  and
      Abdelali, Ahmad  and
      Abudalfa, Shadi",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-nejlt/2026.abjadnlp-1.62/",
    doi = "10.18653/v1/2026.abjadnlp-1.62",
    pages = "493--496",
    abstract = "The rapid advancement of large language models necessitates robust methods for detecting AI-generated Arabic text. This paper presents our system for distinguishing human-written from machine-generated Arabic content. We propose a weighted ensemble combining AraBERTv2 and BERT-base-arabic, trained via 5-fold stratified cross-validation with class-balanced loss functions. Our methodology incorporates Arabic text normalization, strategic data augmentation using 16,678 samples from external scientific abstracts, and threshold optimization prioritizing recall. On the official test set, our system achieved an F1-score of 0.763, an accuracy of 0.695, a precision of 0.624, and a recall of 0.980, demonstrating strong detection of machine-generated texts with minimal false negatives at the cost of elevated false positives. Analysis reveals critical insights into precision-recall trade-offs and challenges in cross-domain generalization for Arabic AI text detection."
}