Aligning Cultural Behaviors in Arabic Language Models via Preferences

Abstract:Arabic LLMs have improved rapidly, but aligning them with country-specific cultural behavioral preferences remains underexplored. Previous work has focused primarily on instruction-tuning for factual knowledge, overlooking nuanced cultural behaviors. We propose ArabPref, which to our knowledge is the first large-scale pan-Arab preference data set for the alignment of cultural behavior conditioned on the country. Our contribution includes two resources in English and Modern Standard Arabic: a culturally grounded preference dataset for training and evaluation, and a multiple-choice benchmark designed to assess culturally aligned behavior across nations. All test data are validated by native speakers to ensure authenticity. To optimize cultural alignment, we experiment with post-training methods across multilingual and Arabic-centric language models, evaluating performance on both generation tasks and our cultural multiple-choice benchmark. By releasing an evaluation benchmark, we provide a foundation for systematically studying and improving culturally aligned behavior in language models.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.25362 [cs.CL]

(or arXiv:2610.25362v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.25362 Focus to learn more arXiv-issued DOI via DataCite

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