The paper introduces CAPRI, a dataset of conversations designed to test whether large language models (LLMs) can adapt their responses to users’ cultural backgrounds. Experiments show that while LLMs can infer a user’s cultural context and recall relevant conventions, they frequently fail to apply this knowledge when generating answers unless explicitly prompted to do so. The study also finds that LLMs’ cultural adaptations improve as more cues are provided, yet their default priors are not culture‑neutral and sometimes reflect the model’s country of origin.
By Yisong Miao, Jian Zhu, Vered Shwartz
arXiv:2608.22411v1 Announce Type: new
Abstract: Social interaction increasingly takes place in multicultural settings, where individuals may draw on multiple cultural influences and adapt their commu...
By Chongyuan Dai, Yaling Shen, Shengeng Tang, Hui Ma, Jinpeng Hu
arXiv:2601. 14063v2 Announce Type: replace-cross Abstract: Cross-cultural competence in large language models (LLMs) requires understanding and adapting Culture-Specific Items (CSIs) across varying cultural contexts.
By Mohsinul Kabir, Tasnim Ahmed, Md Mezbaur Rahman, Shaoxiong Ji, Hassan Alhuzali, Yuechen Jiang, Jimin Huang, Sophia Ananiadou
arXiv:2609.00565v1 Announce Type: cross
Abstract: Cultural fine-tuning has become the de facto paradigm for building culture-aware large language models (LLMs), yet existing optimization exclusively...
By Jingshen Zhang, Shaoyang Xu, Wenxuan Zhang
arXiv:2605.12515v3 Announce Type: replace
Abstract: Multilingual large language models (MLLMs) often answer culturally ambiguous questions inconsistently across languages, even for a fixed user perso...
By Lucas Resck, Isabelle Augenstein, Anna Korhonen
arXiv:2608.30873v1 Announce Type: cross
Abstract: LLMs are increasingly used for interpersonal advice and as tools for studying social behavior across languages and cultures. A common shortcut for el...
By Jinhee Won, Xinlan Emily Hu