@inproceedings{jiang-etal-2025-language,
title = "Can Language Models Reason about Individualistic Human Values and Preferences?",
author = "Jiang, Liwei and
Sorensen, Taylor and
Levine, Sydney and
Choi, Yejin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.336/",
doi = "10.18653/v1/2025.acl-long.336",
pages = "6757--6794",
ISBN = "979-8-89176-251-0",
abstract = "Recent calls for pluralistic alignment emphasize that AI systems should address the diverse needs of all people. Yet, efforts in this space often require sorting people into fixed buckets of pre-specified diversity-defining dimensions (e.g., demographics), risking smoothing out individualistic variations or even stereotyping. To achieve an authentic representation of diversity that respects individuality, we propose individualistic alignment. While individualistic alignment can take various forms, in this paper, we introduce IndieValueCatalog, a dataset transformed from the influential World Values Survey (WVS), to study language models (LMs) on the specific challenge of individualistic value reasoning. Given a sample of an individual{'}s value-expressing statements, models are tasked with predicting their value judgments in novel cases. With IndieValueCatalog, we reveal critical limitations in frontier LMs' abilities to predict individualistic values with accuracies only ranging between 55{\%} to 65{\%}. Moreover, our results highlight that a precise description of individualistic values cannot be approximated only via demographic information. Finally, we train a series of IndieValueReasoners to reveal new patterns and dynamics into global human values."
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<abstract>Recent calls for pluralistic alignment emphasize that AI systems should address the diverse needs of all people. Yet, efforts in this space often require sorting people into fixed buckets of pre-specified diversity-defining dimensions (e.g., demographics), risking smoothing out individualistic variations or even stereotyping. To achieve an authentic representation of diversity that respects individuality, we propose individualistic alignment. While individualistic alignment can take various forms, in this paper, we introduce IndieValueCatalog, a dataset transformed from the influential World Values Survey (WVS), to study language models (LMs) on the specific challenge of individualistic value reasoning. Given a sample of an individual’s value-expressing statements, models are tasked with predicting their value judgments in novel cases. With IndieValueCatalog, we reveal critical limitations in frontier LMs’ abilities to predict individualistic values with accuracies only ranging between 55% to 65%. Moreover, our results highlight that a precise description of individualistic values cannot be approximated only via demographic information. Finally, we train a series of IndieValueReasoners to reveal new patterns and dynamics into global human values.</abstract>
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%0 Conference Proceedings
%T Can Language Models Reason about Individualistic Human Values and Preferences?
%A Jiang, Liwei
%A Sorensen, Taylor
%A Levine, Sydney
%A Choi, Yejin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F jiang-etal-2025-language
%X Recent calls for pluralistic alignment emphasize that AI systems should address the diverse needs of all people. Yet, efforts in this space often require sorting people into fixed buckets of pre-specified diversity-defining dimensions (e.g., demographics), risking smoothing out individualistic variations or even stereotyping. To achieve an authentic representation of diversity that respects individuality, we propose individualistic alignment. While individualistic alignment can take various forms, in this paper, we introduce IndieValueCatalog, a dataset transformed from the influential World Values Survey (WVS), to study language models (LMs) on the specific challenge of individualistic value reasoning. Given a sample of an individual’s value-expressing statements, models are tasked with predicting their value judgments in novel cases. With IndieValueCatalog, we reveal critical limitations in frontier LMs’ abilities to predict individualistic values with accuracies only ranging between 55% to 65%. Moreover, our results highlight that a precise description of individualistic values cannot be approximated only via demographic information. Finally, we train a series of IndieValueReasoners to reveal new patterns and dynamics into global human values.
%R 10.18653/v1/2025.acl-long.336
%U https://aclanthology.org/2025.acl-long.336/
%U https://doi.org/10.18653/v1/2025.acl-long.336
%P 6757-6794
Markdown (Informal)
[Can Language Models Reason about Individualistic Human Values and Preferences?](https://aclanthology.org/2025.acl-long.336/) (Jiang et al., ACL 2025)
ACL