Can LLM s Express Personality Across Cultures? Introducing C ultural P ersonas for Evaluating Trait Alignment (2025)

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 , 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 how closely three models’ personality distributions align to real human populations through two evaluation settings: multiple-choice and open-ended response formats. Our results show– 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. surfaces meaningful modulate 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 will pave the way for more socially intelligent and globally adaptive LLMs. Datasets and code are available at: https://github.com/limenlp/CulturalPersonas.

## introduction from customer support agents to personal tutors, large language models (llms) are central to interactive technologies that engage users daily. these systems are increasingly expected to display not just fluency or task competence, but personality: the ability to convey a psychological profile that users perceive as trustworthy and relatable. in psychology and nlp, the big five (ocean) framework is widely used to model personality through five traits-openness, conscientiousness, extraversion, agreeableness, and neuroticism-that represent consistent patterns of thought, behavior, and emotion. recent work evaluates llms using psychometric tools like the big five inventory (bfi) @xcite and the international personality item pool (ipip) @xcite , via self-reflective statements (e.g., "i talk a lot", "i make people feel at ease") @xcite @xcite . moving beyond static questionnaires, newer benchmarks such as trait @xcite and big5chat @xcite embed personality traits into dialogue or situational prompts, enabling more behaviorally grounded evaluations. while these tools offer a useful starting point, they fall short of capturing how personality is expressed through behavior in real-world contexts. moreover, they assume that personality is culturally universal despite decades of work in cultural psychology, which shows that trait expression is deeply shaped by local norms, values, and communicative styles @xcite @xcite . for example, in western cultures such as the united states, assertiveness may reflect high openness. however, in nations such as japan, this same quality may be seen as disruptive; instead, traits such as harmony and indirectness are stronger and more preferred indicators of openness. evaluating llms without accounting for these distinctions risks reinforcing western behavioral norms and misrepresenting how personality is interpreted across cultures. to address this gap, we introduce culturalpersonas-the first large-scale benchmark for evaluating personality expression in llms through culturally grounded, behavior-rich scenarios. cultur-alpersonas comprises 3,000 situational prompts spanning six culturally diverse countries: brazil, india, japan, saudi arabia, south africa, and the united states. each prompt targets one of the big five traits by embedding it in realistic, locally situated social contexts that reflect cultural values-i.e., shared behavioral norms and expectations that shape how individuals act within a society. scenarios are generated via a retrievalaugmented generation (rag) pipeline @xcite seeded with academic cultural texts and subsequently validated by native annotators for cultural fidelity (see figure 1 and §3 .3). we use culturalpersonas to measure the personality profiles of three models-gpt-4o-mini, llama-3-8b, and qwen2-7b-across two evaluation formats: multiple-choice selection and openended generation and compare alignment to real human populations from diverse cultural backgrounds. our contributions are fourfold: and trait, elicits more expressive and lexically diverse outputs, and surfaces model-specific differences in cultural adaptability ( §5). ## experimental setup we evaluate whether persona-prompted llms can express personality traits in ways that align with human behavior across cultures. specifically, we assess whether models can reproduce personality trait distributions that resemble real-world data across six countries. we conduct evaluations under two complementary formats: (1) multiple-choice selection (mcs) ( §4.3.1), where models select likertstyle natural-language options e.g., "i agree"/"i disagree" (bfi, ipip) or behavioral statements like "i prefer to stay away from crowds" (trait, big5chat, culturalpersonas); and (2) open-ended generation (oeg) ( §4.3.2), where models generate free-form behavioral responses that are subsequently mapped to trait scores. controlled v. generative settings. mcs provides a controlled evaluation with predefined answers, enabling consistent scoring and tractable alignment analysis. this format lets us evaluate shifts in personality expression across cultures. in contrast, oeg enables more naturalistic language that reflects free-form interaction in applications like chatbots and negotiation agents, providing richer signals to analyze behavioral nuance, lexical variation, and cultural grounding. ground truth human distributions. personality distributions are drawn from johnson's ipip-neo dataset @xcite , which contains 307,313 ipip-120 responses from real-world global populations. we extract per-country trait distributions for the six countries in culturalpersonas: usa (212,625), brazil (661), india (2,841), south africa (927), saudi arabia (98), and japan (398). ## evaluating multi-cultural personalities in llms we evaluate how well llms express personality traits in culturally grounded settings. our analysis proceeds in four parts: (1) trait alignment with human personality norms in mcs and oeg, (2) linguistic expressivity in free-form responses, (3) cross-model differences in cultural adaptation, and (4) the role of cultural priming in trait elicitation. ## conclusion & future work in llms across diverse global regions. our results show that culturalpersonas consistently improves alignment and expressivity over prior benchmarks, particularly for traits whose distributions vary significantly across cultures. furthermore, culturalpersonas reveals that different models vary in their ability to express personality, with some traits noticeably harder to portray. while traditional psychometric tests remain valuable for measuring llms' values and beliefs, our findings highlight the importance of behaviorally grounded, culturally specific scenarios to elicit realistic trait expressions in varied contexts. we hope culturalpersonas will enable future research into how llms perform behaviorally across cultures, promote more emic and context-rich personality modeling, and support the development of more inclusive, adaptive alignment methods. future work includes expanding coverage to additional cultures, incorporating multilingual and subcultural variation, and exploring preference tuning or human-in-the-loop adaptation for culturally aware generation.