大型语言模型能否跨文化表达个性？引入CulturalPersonas评估特质对齐
Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment
AI总结：
本文提出CulturalPersonas基准，含3000个跨六国场景问题，评估LLM个性表达，使Wasserstein距离降低超20%，促进文化对齐。
摘要：
随着大型语言模型（LLM）成为从辅导到心理健康等交互式应用的核心，以文化适宜的方式表达个性的能力日益重要。尽管近期研究已探索LLM的个性评估，但大多忽视了文化与个性之间的相互作用。为解决这一问题，我们引入了CulturalPersonas，这是首个经过人工验证的大规模基准，用于评估LLM在文化扎根、行为丰富的语境中的个性表达。我们的数据集涵盖六个不同国家的3,000个基于场景的问题，旨在通过植根于当地价值观的日常场景引发个性表现。我们使用多项选择和开放式回答两种格式评估了三个LLM。结果表明，与现有基准相比，CulturalPersonas提高了与特定国家人类个性分布的一致性（跨模型和国家的Wasserstein距离减少超过20%），并引发更具表现力、文化连贯的输出。CulturalPersonas针对文化扎根的提示产生了有意义的情境化特质输出，为将LLM与全球行为规范对齐提供了新方向。通过弥合个性表达与文化细微差别，我们设想CulturalPersonas将为更具社会智能和全球适应性的LLM铺平道路。
英文摘要：
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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
