@inproceedings{guo-etal-2025-care,
title = "{CARE}: Multilingual Human Preference Learning for Cultural Awareness",
author = "Guo, Geyang and
Naous, Tarek and
Wakaki, Hiromi and
Nishimura, Yukiko and
Mitsufuji, Yuki and
Ritter, Alan and
Xu, Wei",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1669/",
pages = "32854--32883",
ISBN = "979-8-89176-332-6",
abstract = "Language Models (LMs) are typically tuned with human preferences to produce helpful responses, but the impact of preference tuning on the ability to handle culturally diverse queries remains understudied. In this paper, we systematically analyze how native human cultural preferences can be incorporated into the preference learning process to train more culturally aware LMs. We introduce \textbf{CARE}, a multilingual resource containing 3,490 culturally specific questions and 31.7k responses with human judgments. We demonstrate how a modest amount of high-quality native preferences improves cultural awareness across various LMs, outperforming larger generic preference data. Our analyses reveal that models with stronger initial cultural performance benefit more from alignment, leading to gaps among models developed in different regions with varying access to culturally relevant data. CARE is publicly available at \url{https://github.com/Guochry/CARE}."
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<abstract>Language Models (LMs) are typically tuned with human preferences to produce helpful responses, but the impact of preference tuning on the ability to handle culturally diverse queries remains understudied. In this paper, we systematically analyze how native human cultural preferences can be incorporated into the preference learning process to train more culturally aware LMs. We introduce CARE, a multilingual resource containing 3,490 culturally specific questions and 31.7k responses with human judgments. We demonstrate how a modest amount of high-quality native preferences improves cultural awareness across various LMs, outperforming larger generic preference data. Our analyses reveal that models with stronger initial cultural performance benefit more from alignment, leading to gaps among models developed in different regions with varying access to culturally relevant data. CARE is publicly available at https://github.com/Guochry/CARE.</abstract>
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%0 Conference Proceedings
%T CARE: Multilingual Human Preference Learning for Cultural Awareness
%A Guo, Geyang
%A Naous, Tarek
%A Wakaki, Hiromi
%A Nishimura, Yukiko
%A Mitsufuji, Yuki
%A Ritter, Alan
%A Xu, Wei
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F guo-etal-2025-care
%X Language Models (LMs) are typically tuned with human preferences to produce helpful responses, but the impact of preference tuning on the ability to handle culturally diverse queries remains understudied. In this paper, we systematically analyze how native human cultural preferences can be incorporated into the preference learning process to train more culturally aware LMs. We introduce CARE, a multilingual resource containing 3,490 culturally specific questions and 31.7k responses with human judgments. We demonstrate how a modest amount of high-quality native preferences improves cultural awareness across various LMs, outperforming larger generic preference data. Our analyses reveal that models with stronger initial cultural performance benefit more from alignment, leading to gaps among models developed in different regions with varying access to culturally relevant data. CARE is publicly available at https://github.com/Guochry/CARE.
%U https://aclanthology.org/2025.emnlp-main.1669/
%P 32854-32883
Markdown (Informal)
[CARE: Multilingual Human Preference Learning for Cultural Awareness](https://aclanthology.org/2025.emnlp-main.1669/) (Guo et al., EMNLP 2025)
ACL
- Geyang Guo, Tarek Naous, Hiromi Wakaki, Yukiko Nishimura, Yuki Mitsufuji, Alan Ritter, and Wei Xu. 2025. CARE: Multilingual Human Preference Learning for Cultural Awareness. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 32854–32883, Suzhou, China. Association for Computational Linguistics.