Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment

• URL: http://arxiv.org/abs/2506.05670v1
• Date: Fri, 06 Jun 2025 01:33:19 GMT
• Title: Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment
• Authors: Priyanka Dey, Yugal Khanter, Aayush Bothra, Jieyu Zhao, Emilio Ferrara,
• Abstract summary: We introduce CulturalPersonas, the first large-scale benchmark with human validation for evaluating personality expression in behaviorally rich contexts.<n>Our dataset spans 3,000 scenario-based questions across six diverse countries, designed to elicit personality through everyday scenarios rooted in local values.<n>Our results show that CulturalPersonas improves alignment with country-specific human personality distributions.
• Score: 16.702098536881127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As LLMs become central to interactive applications, ranging from tutoring to mental health, the ability to express personality in culturally appropriate ways is increasingly important. While recent works have explored personality evaluation of LLMs, they largely overlook the interplay between culture and personality. To address this, we introduce CulturalPersonas, the first large-scale benchmark with human validation for evaluating LLMs' personality expression in culturally grounded, behaviorally rich contexts. Our dataset spans 3,000 scenario-based questions across six diverse countries, designed to elicit personality through everyday scenarios rooted in local values. We evaluate three LLMs, using both multiple-choice and open-ended response formats. Our results show that CulturalPersonas improves alignment with country-specific human personality distributions (over a 20% reduction in Wasserstein distance across models and countries) and elicits more expressive, culturally coherent outputs compared to existing benchmarks. CulturalPersonas surfaces meaningful modulated trait outputs in response to culturally grounded prompts, offering new directions for aligning LLMs to global norms of behavior. By bridging personality expression and cultural nuance, we envision that CulturalPersonas will pave the way for more socially intelligent and globally adaptive LLMs.

Related papers

• From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs [57.43233760384488]
  Adapting cultural values in Large Language Models (LLMs) presents significant challenges.<n>Prior work primarily aligns LLMs with different cultural values using World Values Survey (WVS) data.<n>In this paper, we investigate WVS-based training for cultural value adaptation and find that relying solely on survey data cane cultural norms and interfere with factual knowledge.
  arXiv  Detail & Related papers  (2025-05-22T09:00:01Z)
• Localized Cultural Knowledge is Conserved and Controllable in Large Language Models [20.411764699679058]
  We show that explicitly providing cultural context in prompts significantly improves the models' ability to generate culturally localized responses.<n>Despite the explicit prompting benefit, however, the answers reduce in diversity and tend toward stereotypes.<n>We identify an explicit cultural customization vector, conserved across all non-English languages, which enables LLMs to be steered from the synthetic English cultural world-model toward each non-English cultural world.
  arXiv  Detail & Related papers  (2025-04-14T12:53:58Z)
• An Evaluation of Cultural Value Alignment in LLM [27.437888319382893]
  We conduct the first large-scale evaluation of LLM culture assessing 20 countries' cultures and languages across ten LLMs.<n>Our findings show that the output over all models represents a moderate cultural middle ground.<n> Deeper investigation sheds light on the influence of model origin, prompt language, and value dimensions on cultural output.
  arXiv  Detail & Related papers  (2025-04-11T09:13:19Z)
• CAReDiO: Cultural Alignment of LLM via Representativeness and Distinctiveness Guided Data Optimization [50.90288681622152]
  Large Language Models (LLMs) more deeply integrate into human life across various regions.<n>Existing approaches develop culturally aligned LLMs through fine-tuning with culture-specific corpora.<n>We introduce CAReDiO, a novel cultural data construction framework.
  arXiv  Detail & Related papers  (2025-04-09T13:40:13Z)
• Cultural Learning-Based Culture Adaptation of Language Models [70.1063219524999]
  Adapting large language models (LLMs) to diverse cultural values is a challenging task.<n>We present CLCA, a novel framework for enhancing LLM alignment with cultural values based on cultural learning.
  arXiv  Detail & Related papers  (2025-04-03T18:16:26Z)
• Self-Pluralising Culture Alignment for Large Language Models [36.689491885394034]
  We propose CultureSPA, a framework that allows large language models to align to pluralistic cultures. By comparing culture-aware/unaware outputs, we are able to detect and collect culture-related instances. Extensive experiments demonstrate that CultureSPA significantly improves the alignment of LLMs to diverse cultures without compromising general abilities.
  arXiv  Detail & Related papers  (2024-10-16T19:06:08Z)
• How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions [9.275967682881944]
  It is critical to understand whether Large Language Models showcase different values to the user based on the stereotypical values of a user's known country. We prompt different LLMs with a series of advice requests based on 5 Hofstede Cultural Dimensions. We found that LLMs can differentiate between one side of a value and another, as well as understand that countries have differing values.
  arXiv  Detail & Related papers  (2024-06-21T00:58:01Z)
• Translating Across Cultures: LLMs for Intralingual Cultural Adaptation [12.5954253354303]
  We define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs. We analyze possible issues with automatic adaptation. We hope that this paper will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.
  arXiv  Detail & Related papers  (2024-06-20T17:06:58Z)
• CulturePark: Boosting Cross-cultural Understanding in Large Language Models [63.452948673344395]
  This paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection. It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs. We evaluate these models across three downstream tasks: content moderation, cultural alignment, and cultural education.
  arXiv  Detail & Related papers  (2024-05-24T01:49:02Z)
• Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense [98.09670425244462]
  Large language models (LLMs) have demonstrated substantial commonsense understanding. This paper examines the capabilities and limitations of several state-of-the-art LLMs in the context of cultural commonsense tasks.
  arXiv  Detail & Related papers  (2024-05-07T20:28:34Z)
• NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models [26.64843536942309]
  Large language models (LLMs) may need to adapt outputs to user values and cultures, not just know about them.<n>We introduce NormAd, an evaluation framework to assess LLMs' cultural adaptability.<n>We create NormAd-Eti, a benchmark of 2.6k situational descriptions representing social-etiquette related cultural norms from 75 countries.
  arXiv  Detail & Related papers  (2024-04-18T18:48:50Z)
• CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting [73.94059188347582]
  We uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations. We discover that culture-conditioned generation consist of linguistic "markers" that distinguish marginalized cultures apart from default cultures.
  arXiv  Detail & Related papers  (2024-04-16T00:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.