Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World

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.

Citation

S. Almheiri, R. Elbadry, M. Attia, C. Wang, P. Nakov, T. Baldwin, F. Koto, "Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World," 2025, pp. 4593-4614.

Conference

Findings of the Association for Computational Linguistics: EMNLP 2025

Source

Findings of the Association for Computational Linguistics: EMNLP 2025

Publisher

Association for Computational Linguistics (ACL)

DOI

10.18653/v1/2025.findings-emnlp.247

Additional links

https://aclanthology.org/2025.findings-emnlp.247/