@inproceedings{adilazuarda-etal-2025-surveys,
title = "From Surveys to Narratives: Rethinking Cultural Value Adaptation in {LLM}s",
author = "Adilazuarda, Farid and
Liu, Chen Cecilia and
Gurevych, Iryna and
Aji, Alham Fikri",
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.912/",
doi = "10.18653/v1/2025.emnlp-main.912",
pages = "18052--18079",
ISBN = "979-8-89176-332-6",
abstract = "Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and data limitations. Previous work aligns LLMs with different cultures using survey data, primarily from the World Values Survey (WVS). However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for tasks like offensiveness classification. In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge. To address these issues, we propose augmenting WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd. Our experiments across multiple cultures show that this approach captures more enhances differentiated cultural values and improves downstream classification performances."
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<abstract>Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and data limitations. Previous work aligns LLMs with different cultures using survey data, primarily from the World Values Survey (WVS). However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for tasks like offensiveness classification. In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge. To address these issues, we propose augmenting WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd. Our experiments across multiple cultures show that this approach captures more enhances differentiated cultural values and improves downstream classification performances.</abstract>
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%0 Conference Proceedings
%T From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs
%A Adilazuarda, Farid
%A Liu, Chen Cecilia
%A Gurevych, Iryna
%A Aji, Alham Fikri
%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 adilazuarda-etal-2025-surveys
%X Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and data limitations. Previous work aligns LLMs with different cultures using survey data, primarily from the World Values Survey (WVS). However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for tasks like offensiveness classification. In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge. To address these issues, we propose augmenting WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd. Our experiments across multiple cultures show that this approach captures more enhances differentiated cultural values and improves downstream classification performances.
%R 10.18653/v1/2025.emnlp-main.912
%U https://aclanthology.org/2025.emnlp-main.912/
%U https://doi.org/10.18653/v1/2025.emnlp-main.912
%P 18052-18079
Markdown (Informal)
[From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs](https://aclanthology.org/2025.emnlp-main.912/) (Adilazuarda et al., EMNLP 2025)
ACL