{
  "id": 5866,
  "url": "https://arxiv.org/abs/2604.12851v1",
  "title": "Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment",
  "summary": "Despite their global prevalence, many Large Language Models (LLMs) are aligned to a monolithic, often Western-centric set of values. This paper investigates the more challenging task of fine-grained value alignment: examining whether LLMs can emulate the distinct cultural values of demographic subgroups. Using Singapore as a case study and the World Values Survey (WVS), we examine the value landscape and show that even state-of-the-art models like GPT-4.1 achieve only 57.4% accuracy in predictin",
  "authors": "Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi, Jing Yao, Xing Xie, Nancy F. Chen et al.",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,finance-investment",
  "orgs": "openai",
  "regions": null,
  "published_at": "2026-04-14T15:06:13.000Z",
  "fetched_at": "2026-07-14T16:32:06.465Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5866",
  "original_url": "https://arxiv.org/abs/2604.12851v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}