Kashif-AI at AbjadGenEval Shared Task: A Transformer-based Approach for Arabic AI-Generated Text Detection
Résumé
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 nearperfect 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.
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