Abstract: This study investigates the effectiveness of large language models (LLMs) in detecting Arabic hate speech, with a particular focus on prompt-based learning and the sociolinguistic challenges of Saudi dialects. We evaluate four LLMs, GPT-4o, LLaMA3, Gemma2, and ALLaM, using zero-shot, one-shot, and three-shot prompting strategies. The results show that all models benefit from in-context examples, with GPT-4o achieving the highest overall performance across all prompting settings. A detailed error analysis reveals persistent challenges, particularly in detecting implicit hate, handling dialectal variation, and interpreting culturally embedded expressions. We also highlight limitations related to topic bias and annotation ambiguity, which further complicate model evaluation. Overall, the findings offer key insights for evaluating LLMs in low-resource settings and addressing the unique linguistic complexities of Arabic dialects.

Omaima Fallatah. “Benchmarking Large Language Models for Hate Speech Detection in Arabic Dialects: Focus on the Saudi Dialects”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.9 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160978

@article{Fallatah2025,
title = {Benchmarking Large Language Models for Hate Speech Detection in Arabic Dialects: Focus on the Saudi Dialects},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160978},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160978},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {9},
author = {Omaima Fallatah}
}