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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2604.04732 |
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| _version_ | 1866910105870532608 |
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| author | Chang, Yuan Qu, Jiaming Li, Zhu |
| author_facet | Chang, Yuan Qu, Jiaming Li, Zhu |
| contents | Large language models (LLMs) are often described as multilingual because they can understand and respond in many languages. However, speaking a language is not the same as reasoning within a culture. This distinction motivates a critical question: do LLMs truly conduct culture-aware reasoning? This paper presents a preliminary computational audit of cultural inclusivity in a creative writing task. We empirically examine whether LLMs act as culturally diverse creative partners or merely as cultural translators that leverage a dominant conceptual framework with localized expressions. Using a metaphor generation task spanning five cultural settings and several abstract concepts as a case study, we find that the model exhibits stereotyped metaphor usage for certain settings, as well as Western defaultism. These findings suggest that merely prompting an LLM with a cultural identity does not guarantee culturally grounded reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04732 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs Chang, Yuan Qu, Jiaming Li, Zhu Computation and Language Artificial Intelligence Large language models (LLMs) are often described as multilingual because they can understand and respond in many languages. However, speaking a language is not the same as reasoning within a culture. This distinction motivates a critical question: do LLMs truly conduct culture-aware reasoning? This paper presents a preliminary computational audit of cultural inclusivity in a creative writing task. We empirically examine whether LLMs act as culturally diverse creative partners or merely as cultural translators that leverage a dominant conceptual framework with localized expressions. Using a metaphor generation task spanning five cultural settings and several abstract concepts as a case study, we find that the model exhibits stereotyped metaphor usage for certain settings, as well as Western defaultism. These findings suggest that merely prompting an LLM with a cultural identity does not guarantee culturally grounded reasoning. |
| title | Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.04732 |