Cross-Cultural Transfer of Commonsense Reasoning in LLM s: Evidence from the A rab World (2025)

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.

## introduction large language models (llms) are increasingly being deployed across diverse cultural contexts; yet, they often reflect a western-centric worldview, misaligning with local customs, values, and norms @xcite @xcite . prior studies have explored broad east-west cultural misalignments in llms @xcite , but little is known about how these models handle intra-regional cultural variation, such as those across the 22 arab countries. for example, despite sharing linguistic ties, emirati culture differs significantly from egyptian or syrian traditions in food, festivals, and gender roles. however, most arabic llms are trained on translated english data or regionally-aggregated corpora @xcite , potentially flattening these cultural distinctions. a key challenge in aligning llms with countryspecific cultural knowledge is the uneven availability of data. high-population countries such as egypt provide vastly more online content than smaller ones like the uae, resulting in cultural underrepresentation. this motivates a question: can cultural knowledge from one country be transferred to benefit another one with limited resources? in this paper, we investigate the feasibility of cross-cultural commonsense transfer within the arab world (see figure 1 ). we focus on this region because it combines a shared linguistic foundation with rich cultural diversity, and because most existing arabic llms are trained on aggregated or translated data that risk obscuring local distinctions. specifically, we evaluate whether aligning an llm to the culture of one arab country can enhance its performance on others through two lightweight alignment strategies: in-context learning (icl) and demonstration-based iterative task tuning optimization (ditto) @xcite (see figure 2 ). while icl is a strong few-shot baseline, ditto offers a reinforcement learning alternative that requires only a handful of high-quality demonstrations, making it particularly suitable for low-resource cultural domains. we conduct experiments on the arabculture dataset @xcite , covering 13 countries and 3.2k examples across domains such as food, rituals, relationships, and social norms. using only 12 culture-specific demonstrations per source country, we evaluate transfer to unseen target cultures across four llms (qwen2.5, gemma-2, allam, and silma) @xcite @xcite . beyond performance, we examine whether cross-cultural gains can be predicted from geographic proximity or cross-country data similarity, and whether alignment reshapes latent cultural representations. our contributions are as follows: ## methodology we describe the data, the alignment procedures (icl and ditto), the sampling strategies, and the evaluation protocol for measuring cross-cultural transfer (figure 2 ). we emphasize icl and ditto because pilot experiments (figure 3 ) showed they surpassed instruction fine-tuning and vanilla dpo on our tasks, warranting deeper exploration. ## results our experiments reveal key findings across the four language models, as evident from ## conclusion our study demonstrated that llms can effectively achieve cross-cultural adaptation using lightweight alignment methods such as icl and ditto, producing consistent gains in arab countries, even with limited and culturally specific data. the results of our experiments indicated strong crosscultural transfer, where training on one country's dataset can significantly improve accuracy in other countries, with gains often exceeding 15-20% absolute across multilingual models. some country pairs show modest gains even at large distances, while others see minimal improvement despite close proximity, suggesting that cultural proximity is not strictly tied to geographic location. in contrast, we observed an overall positive correlation between cultural similarity and accuracy improvement across multilingual models, suggesting greater transferability between countries that are more culturally similar. probing analysis further showed that targeted alignment enhances cultural encoding (e.g., for the uae) without harming overall performance, highlighting the feasibility and benefits of culturally adaptive nlp in multilingual settings. in general, our results highlight that lightweight alignment methods can effectively align on the cultural commonsense reasoning task by incorporating region-specific cultural demonstrations. whether through icl or ditto, llms can learn robust cultural representations that transfer to new countries. therefore, this work reinforces the notion that cross-cultural adaptation is feasible and beneficial in multilingual nlp settings, particularly in the arab world.