EMNLP Findings2025
Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment
Priyanka Dey, Aayush Bothra, Yugal Khanter, Jieyu Zhao, Emilio Ferrara
摘要
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 Cul-turalPersonas, 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-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 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 CulturalPersonas will pave the way for more socially intelligent and globally adaptive LLMs. Datasets and code are available at: https://github.com/limenlp/CulturalPersonas.
问 Lune
尽管问,回答会引用原文。
Lune 会读完全文和相关的 Paper,每个回答都附上所依据的原文段落。
引用它的顶会 Paper1
它引用的 Paper4
- Evaluating and Inducing Personality in Pre-trained Language ModelsGuangyuan Jiang, Manjie Xu, Song-Chun Zhu, Wenjuan Han 等NeurIPS 2023 · 192 次引用
- Faithful Persona-based Conversational Dataset Generation with Large Language ModelsPegah Jandaghi, XiangHai Sheng, Xinyi Bai, Jay Pujara 等ACL Findings 2024 · 10 次引用
- Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual RobustnessBryan Li, Fiona Luo, Samar Haider, Adwait Agashe 等ACL Findings 2025 · 3 次引用
- Neuron based Personality Trait Induction in Large Language ModelsJia Deng, Tianyi Tang, Yanbin Yin, Wenhao Yang 等ICLR 2025
相关 Paper
- BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded DataWenkai Li, Jiarui Liu, Andy Liu, Xuhui Zhou 等ACL 2025
- MPTA: MultiTask Personalization AssessmentMatthieu Tehenan, Eric Chamoun, Andreas VlachosEMNLP Findings 2025
- From 1, 000, 000 Users to Every User: Scaling Up Personalized Preference for User-level AlignmentJia-Nan Li, Jian Guan, Songhao Wu, Wei Wu 等ACL 2026
- PersonalLLM: Tailoring LLMs to Individual PreferencesThomas P. Zollo, Andrew Wei Tung Siah, Naimeng Ye, Ang Li 等ICLR 2025
- MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding EvaluationWeihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu 等ACL 2026
