@inproceedings{almheiri-etal-2025-cross,
    title = "Cross-Cultural Transfer of Commonsense Reasoning in {LLM}s: Evidence from the {A}rab World",
    author = "Almheiri, Saeed  and
      Elbadry, Rania  and
      Attia, Mena  and
      Wang, Chenxi  and
      Nakov, Preslav  and
      Baldwin, Timothy  and
      Koto, Fajri",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.247/",
    doi = "10.18653/v1/2025.findings-emnlp.247",
    pages = "4593--4614",
    ISBN = "979-8-89176-335-7",
    abstract = "Large language models (LLMs) often reflect Western-centric biases, limiting their effectiveness in diverse cultural contexts. Although some work has explored cultural alignment, the potential for cross-cultural transfer, using alignment in one culture to improve performance in others, remains underexplored. This paper investigates cross-cultural transfer of commonsense reasoning within the Arab world, where linguistic and historical similarities coexist with local cultural differences. Using a culturally grounded commonsense reasoning dataset covering 13 Arab countries, we evaluate lightweight alignment methods such as in-context learning (ICL) and demonstration-based reinforcement (DITTO), alongside baselines like supervised fine-tuning (SFT) and direct preference Optimization (DPO). Our results show that merely 12 culture-specific examples from one country can improve performance in others by 10{\%} on average, within multilingual models. In addition, we demonstrate that out-of-culture demonstrations from Indonesia and US contexts can match or surpass in-culture alignment for MCQ reasoning, highlighting cultural commonsense transferability beyond Arab world. These findings demonstrate that efficient cross-cultural alignment is possible and offer a promising approach to adapt LLMs to low-resource cultural settings."
}