Homograph Attacks on Maghreb Sentiment Analyzers
<|reference_start|>Homograph Attacks on Maghreb Sentiment Analyzers: We examine the impact of homograph attacks on the Sentiment Analysis (SA) task of different Arabic dialects from the Maghreb North-African countries. Homograph attacks result in a 65.3% decrease in transformer classification from an F1-score of 0.95 to 0.33 when data is written in "Arabizi". The goal of this study is to highlight LLMs weaknesses' and to prioritize ethical and responsible Machine Learning.<|reference_end|>
@article{qachfar2024homograph,
    title={Homograph Attacks on Maghreb Sentiment Analyzers},
    author={Fatima Zahra Qachfar, Rakesh M. Verma},
    journal={arXiv preprint arXiv:2402.03171},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03171},
    primaryClass={cs.CL cs.CR cs.LG}
}