Are Dialects Better Prompters? A Case Study on Arabic Subjective Text Classification

Leila Moudjari; Farah Benamara

ACL 2025finding0 citations

Are Dialects Better Prompters? A Case Study on Arabic Subjective Text Classification

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.

BibTeX

@inproceedings{moudjari-benamara-2025-dialects,
    title = "Are Dialects Better Prompters? A Case Study on {A}rabic Subjective Text Classification",
    author = "Moudjari, Leila  and
      Benamara, Farah",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.892/",
    doi = "10.18653/v1/2025.findings-acl.892",
    pages = "17356--17371",
    ISBN = "979-8-89176-256-5"
}