@inproceedings{kamruzzaman-etal-2026-woman,
title = "`A Woman is More Culturally Knowledgeable than A Man?': The Effect of Personas on Cultural Norm Interpretation in {LLM}s",
author = "Kamruzzaman, Mahammed and
Nguyen, Hieu Minh and
Hassan, Nazmul and
Kim, Gene Louis",
editor = "Chen, Pinzhen and
Zouhar, Vil{\'e}m and
Hu, Hanxu and
Khanuja, Simran and
Zhu, Wenhao and
Haddow, Barry and
Birch, Alexandra and
Aji, Alham Fikri and
Sennrich, Rico and
Hooker, Sara",
booktitle = "Proceedings of the First Workshop on Multilingual Multicultural Evaluation",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mme-main.15/",
pages = "220--237",
ISBN = "979-8-89176-368-5",
abstract = "As the deployment of large language models (LLMs) expands, there is an increasing demand for personalized LLMs. One method to personalize and guide the outputs of these models is by assigning a persona{---}a role that describes the expected behavior of the LLM (e.g., a man, a woman, an engineer). This study examines whether an LLM{'}s interpretation of social norms varies based on assigned personas and whether these variations stem from embedded biases within the models. In our research, we tested 34 distinct personas from 12 categories (e.g., age, gender, beauty) across four different LLMs. We find that LLMs' cultural norm interpretation varies based on the persona used and that the variations within a persona category (e.g., a fat person and a thin person as in physical appearance group) follow a trend where an LLM with the more socially desirable persona (e.g., a thin person) interprets social norms more accurately than with the less socially desirable persona (e.g., a fat person). While persona-based conditioning can enhance model adaptability, it also risks reinforcing stereotypes rather than providing an unbiased representation of cultural norms. We also discuss how different types of social biases due to stereotypical assumptions of LLMs may contribute to the results that we observe."
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<abstract>As the deployment of large language models (LLMs) expands, there is an increasing demand for personalized LLMs. One method to personalize and guide the outputs of these models is by assigning a persona—a role that describes the expected behavior of the LLM (e.g., a man, a woman, an engineer). This study examines whether an LLM’s interpretation of social norms varies based on assigned personas and whether these variations stem from embedded biases within the models. In our research, we tested 34 distinct personas from 12 categories (e.g., age, gender, beauty) across four different LLMs. We find that LLMs’ cultural norm interpretation varies based on the persona used and that the variations within a persona category (e.g., a fat person and a thin person as in physical appearance group) follow a trend where an LLM with the more socially desirable persona (e.g., a thin person) interprets social norms more accurately than with the less socially desirable persona (e.g., a fat person). While persona-based conditioning can enhance model adaptability, it also risks reinforcing stereotypes rather than providing an unbiased representation of cultural norms. We also discuss how different types of social biases due to stereotypical assumptions of LLMs may contribute to the results that we observe.</abstract>
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%0 Conference Proceedings
%T ‘A Woman is More Culturally Knowledgeable than A Man?’: The Effect of Personas on Cultural Norm Interpretation in LLMs
%A Kamruzzaman, Mahammed
%A Nguyen, Hieu Minh
%A Hassan, Nazmul
%A Kim, Gene Louis
%Y Chen, Pinzhen
%Y Zouhar, Vilém
%Y Hu, Hanxu
%Y Khanuja, Simran
%Y Zhu, Wenhao
%Y Haddow, Barry
%Y Birch, Alexandra
%Y Aji, Alham Fikri
%Y Sennrich, Rico
%Y Hooker, Sara
%S Proceedings of the First Workshop on Multilingual Multicultural Evaluation
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-368-5
%F kamruzzaman-etal-2026-woman
%X As the deployment of large language models (LLMs) expands, there is an increasing demand for personalized LLMs. One method to personalize and guide the outputs of these models is by assigning a persona—a role that describes the expected behavior of the LLM (e.g., a man, a woman, an engineer). This study examines whether an LLM’s interpretation of social norms varies based on assigned personas and whether these variations stem from embedded biases within the models. In our research, we tested 34 distinct personas from 12 categories (e.g., age, gender, beauty) across four different LLMs. We find that LLMs’ cultural norm interpretation varies based on the persona used and that the variations within a persona category (e.g., a fat person and a thin person as in physical appearance group) follow a trend where an LLM with the more socially desirable persona (e.g., a thin person) interprets social norms more accurately than with the less socially desirable persona (e.g., a fat person). While persona-based conditioning can enhance model adaptability, it also risks reinforcing stereotypes rather than providing an unbiased representation of cultural norms. We also discuss how different types of social biases due to stereotypical assumptions of LLMs may contribute to the results that we observe.
%U https://aclanthology.org/2026.mme-main.15/
%P 220-237
Markdown (Informal)
[’A Woman is More Culturally Knowledgeable than A Man?’: The Effect of Personas on Cultural Norm Interpretation in LLMs](https://aclanthology.org/2026.mme-main.15/) (Kamruzzaman et al., MME 2026)
ACL