Incomple/SubgroupValuesSG-outputs

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@inproceedings{tanCanPersonaPromptedLLMs2026,
  title = {Can {{Persona-Prompted LLMs Emulate Subgroup Values}}? {{An Empirical Analysis}} of {{Generalisability}} and {{Fairness}} in {{Cultural Alignment}}},
  shorttitle = {Can {{Persona-Prompted LLMs Emulate Subgroup Values}}?},
  booktitle = {Proceedings of the 64th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})},
  author = {Tan, Bryan Chen Zhengyu and Liu, Zhengyuan and Yi, Xiaoyuan and Yao, Jing and Xie, Xing and Chen, Nancy F. and Lee, Roy Ka-Wei},
  editor = {Liakata, Maria and Moreira, Viviane P. and Zhang, Jiajun and Jurgens, David},
  year = 2026,
  month = jul,
  pages = {24571--24590},
  publisher = {Association for Computational Linguistics},
  address = {San Diego, California, United States},
  doi = {10.18653/v1/2026.acl-long.1127},
  urldate = {2026-09-14},
  abstract = {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 predicting subgroup modal preferences. We construct a dataset of over 20,000 samples to train and e

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dataset_licence({ "id": "Incomple/SubgroupValuesSG-outputs" })
signal: "restrictive"
stated_licence: "cc-by-nc-4.0"
sources: 0