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    <abstract>This paper investigates the effect of dialectal prompting, variations in prompting scrip t and model fine-tuning on subjective classification in Arabic dialects. To this end, we evaluate the performances of 12 widely used open LLMs across four tasks and eight benchmark datasets. Our results reveal that specialized fine-tuned models with Arabic and Arabizi scripts dialectal prompts achieve the best results, which constitutes a novel state of the art in the field.</abstract>
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%0 Conference Proceedings
%T Are Dialects Better Prompters? A Case Study on Arabic Subjective Text Classification
%A Moudjari, Leila
%A Benamara, Farah
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F moudjari-benamara-2025-dialects
%X This paper investigates the effect of dialectal prompting, variations in prompting scrip t and model fine-tuning on subjective classification in Arabic dialects. To this end, we evaluate the performances of 12 widely used open LLMs across four tasks and eight benchmark datasets. Our results reveal that specialized fine-tuned models with Arabic and Arabizi scripts dialectal prompts achieve the best results, which constitutes a novel state of the art in the field.
%R 10.18653/v1/2025.findings-acl.892
%U https://aclanthology.org/2025.findings-acl.892/
%U https://doi.org/10.18653/v1/2025.findings-acl.892
%P 17356-17371

Markdown (Informal)

[Are Dialects Better Prompters? A Case Study on Arabic Subjective Text Classification](https://aclanthology.org/2025.findings-acl.892/) (Moudjari & Benamara, Findings 2025)

• Are Dialects Better Prompters? A Case Study on Arabic Subjective Text Classification (Moudjari & Benamara, Findings 2025)

ACL

• Leila Moudjari and Farah Benamara. 2025. Are Dialects Better Prompters? A Case Study on Arabic Subjective Text Classification. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17356–17371, Vienna, Austria. Association for Computational Linguistics.