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    <abstract>Recent work has shown that LLMs overrepresent dominant cultures, particularly Western ones, while marginalizing others. We investigate whether this affects models’ ability to generate culturally adapted responses by evaluating their use of local measurement units based on the user’s perceived cultural background. We introduce Cultural and Pragmatic Response Inference (CAPRI), a dataset of conversations with varying levels of cultural cues. Experiments with state-of-the-art LLMs show that models can infer cultural background and recall relevant conventions, but often fail to utilize the information to adapt their answers to the relevant cultural conventions, unless explicitly prompted to perform the tasks sequentially. We further evaluate adaptation to the interpretation of time and quantity expressions, two subjective language grounding dimensions that are affected by culture. We find that models increasingly adapt their answers as cultural cues accumulate, but their priors are not culture-neutral, sometimes aligning with the model’s country of origin. Overall, CAPRI provides a resource for future research aimed at narrowing the gap between cultural knowledge and culturally adaptive language generation.</abstract>
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%0 Conference Proceedings
%T LLMs Infer Cultural Context but Fail to Apply It When Responding
%A Miao, Yisong
%A Zhu, Jian
%A Shwartz, Vered
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F miao-etal-2026-llms
%X Recent work has shown that LLMs overrepresent dominant cultures, particularly Western ones, while marginalizing others. We investigate whether this affects models’ ability to generate culturally adapted responses by evaluating their use of local measurement units based on the user’s perceived cultural background. We introduce Cultural and Pragmatic Response Inference (CAPRI), a dataset of conversations with varying levels of cultural cues. Experiments with state-of-the-art LLMs show that models can infer cultural background and recall relevant conventions, but often fail to utilize the information to adapt their answers to the relevant cultural conventions, unless explicitly prompted to perform the tasks sequentially. We further evaluate adaptation to the interpretation of time and quantity expressions, two subjective language grounding dimensions that are affected by culture. We find that models increasingly adapt their answers as cultural cues accumulate, but their priors are not culture-neutral, sometimes aligning with the model’s country of origin. Overall, CAPRI provides a resource for future research aimed at narrowing the gap between cultural knowledge and culturally adaptive language generation.
%U https://aclanthology.org/2026.emnlp-main.1943/
%P 44208-44233

Markdown (Informal)

[LLMs Infer Cultural Context but Fail to Apply It When Responding](https://aclanthology.org/2026.emnlp-main.1943/) (Miao et al., EMNLP 2026)

• LLMs Infer Cultural Context but Fail to Apply It When Responding (Miao et al., EMNLP 2026)

ACL

• Yisong Miao, Jian Zhu, and Vered Shwartz. 2026. LLMs Infer Cultural Context but Fail to Apply It When Responding. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 44208–44233, Budapest, Hungary. Association for Computational Linguistics.