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    <abstract>As Large Language Models (LLMs) become increasingly proficient at generating human-like text, distinguishing between human-written and machine-generated content has become a critical challenge for information integrity. This paper presents Kashif-AI, a system developed for the AbjadGenEval Task 1: AI-Generated Arabic Text Detection. The approach leverages fine-tuned Arabic Pre-trained Language Models (PLMs), specifically MARBERT and CAMeLBERT, to classify news articles. A rigorous ablation study was conducted to evaluate the impact of data augmentation, comparing models trained on the official shared task data against those trained on a combined corpus of over 47,000 samples. While near-perfect performance was observed during validation, the blind test set evaluation revealed a significant generalization gap. Contrary to expectations, data augmentation resulted in performance degradation due to domain shifts. The best-performing configuration, which utilized CAMeLBERT-Mix trained on the original dataset, achieved an F1-score of 66.29% and an Accuracy of 70.5% on the blind test set.</abstract>
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%0 Conference Proceedings
%T Kashif-AI at AbjadGenEval Shared Task: A Transformer-based Approach for Arabic AI-Generated Text Detection
%A Emad Eldin, Fatimah Mohamed
%S Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F emad-eldin-2026-kashif
%X As Large Language Models (LLMs) become increasingly proficient at generating human-like text, distinguishing between human-written and machine-generated content has become a critical challenge for information integrity. This paper presents Kashif-AI, a system developed for the AbjadGenEval Task 1: AI-Generated Arabic Text Detection. The approach leverages fine-tuned Arabic Pre-trained Language Models (PLMs), specifically MARBERT and CAMeLBERT, to classify news articles. A rigorous ablation study was conducted to evaluate the impact of data augmentation, comparing models trained on the official shared task data against those trained on a combined corpus of over 47,000 samples. While near-perfect performance was observed during validation, the blind test set evaluation revealed a significant generalization gap. Contrary to expectations, data augmentation resulted in performance degradation due to domain shifts. The best-performing configuration, which utilized CAMeLBERT-Mix trained on the original dataset, achieved an F1-score of 66.29% and an Accuracy of 70.5% on the blind test set.
%U https://aclanthology.org/2026.abjadnlp-1.60/
%P 483-488

Markdown (Informal)

[Kashif-AI at AbjadGenEval Shared Task: A Transformer-based Approach for Arabic AI-Generated Text Detection](https://aclanthology.org/2026.abjadnlp-1.60/) (Emad Eldin, AbjadNLP 2026)

• Kashif-AI at AbjadGenEval Shared Task: A Transformer-based Approach for Arabic AI-Generated Text Detection (Emad Eldin, AbjadNLP 2026)

ACL

• Fatimah Mohamed Emad Eldin. 2026. Kashif-AI at AbjadGenEval Shared Task: A Transformer-based Approach for Arabic AI-Generated Text Detection. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 483–488, Rabat, Morocco. Association for Computational Linguistics.