Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916964942741504 |
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| author | Almheiri, Saeed Hossam, Rania Attia, Mena Wang, Chenxi Nakov, Preslav Baldwin, Timothy Koto, Fajri |
| author_facet | Almheiri, Saeed Hossam, Rania Attia, Mena Wang, Chenxi Nakov, Preslav Baldwin, Timothy Koto, Fajri |
| contents | 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 in 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 and demonstration-based reinforcement (DITTO), alongside baselines like supervised fine-tuning and direct preference optimization. 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 the 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World Almheiri, Saeed Hossam, Rania Attia, Mena Wang, Chenxi Nakov, Preslav Baldwin, Timothy Koto, Fajri Artificial Intelligence Computation and Language 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 in 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 and demonstration-based reinforcement (DITTO), alongside baselines like supervised fine-tuning and direct preference optimization. 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 the 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. |
| title | Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.19265 |