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        <title>Translating Across Cultures: LLMs for Intralingual Cultural Adaptation</title>
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    <abstract>LLMs are increasingly being deployed for multilingual applications and have demonstrated impressive translation capabilities between several low and high-resource languages. An aspect of translation that often gets overlooked is that of cultural adaptation, or modifying source culture references to suit the target culture. While specialized translation models still outperform LLMs on the machine translation task when viewed from the lens of correctness, they are not sensitive to cultural differences often requiring manual correction. LLMs on the other hand have a rich reservoir of cultural knowledge embedded within its parameters that can be potentially exploited for such applications. In this paper, we define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs for cultural adaptation and analyze their cross-cultural knowledge while connecting related concepts across different cultures. We also analyze possible issues with automatic adaptation. We hope that this task will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.</abstract>
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%0 Conference Proceedings
%T Translating Across Cultures: LLMs for Intralingual Cultural Adaptation
%A Singh, Pushpdeep
%A Patidar, Mayur
%A Vig, Lovekesh
%Y Barak, Libby
%Y Alikhani, Malihe
%S Proceedings of the 28th Conference on Computational Natural Language Learning
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F singh-etal-2024-translating
%X LLMs are increasingly being deployed for multilingual applications and have demonstrated impressive translation capabilities between several low and high-resource languages. An aspect of translation that often gets overlooked is that of cultural adaptation, or modifying source culture references to suit the target culture. While specialized translation models still outperform LLMs on the machine translation task when viewed from the lens of correctness, they are not sensitive to cultural differences often requiring manual correction. LLMs on the other hand have a rich reservoir of cultural knowledge embedded within its parameters that can be potentially exploited for such applications. In this paper, we define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs for cultural adaptation and analyze their cross-cultural knowledge while connecting related concepts across different cultures. We also analyze possible issues with automatic adaptation. We hope that this task will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.
%U https://aclanthology.org/2024.conll-1.30
%P 400-418

Markdown (Informal)

[Translating Across Cultures: LLMs for Intralingual Cultural Adaptation](https://aclanthology.org/2024.conll-1.30) (Singh et al., CoNLL 2024)

• Translating Across Cultures: LLMs for Intralingual Cultural Adaptation (Singh et al., CoNLL 2024)

ACL

• Pushpdeep Singh, Mayur Patidar, and Lovekesh Vig. 2024. Translating Across Cultures: LLMs for Intralingual Cultural Adaptation. In Proceedings of the 28th Conference on Computational Natural Language Learning, pages 400–418, Miami, FL, USA. Association for Computational Linguistics.