@inproceedings{chen-etal-2025-mova,
title = "{M}o{V}a: Towards Generalizable Classification of Human Morals and Values",
author = "Chen, Ziyu and
Sun, Junfei and
Li, Chenxi and
Nguyen, Tuan Dung and
Yao, Jing and
Yi, Xiaoyuan and
Xie, Xing and
Tan, Chenhao and
Xie, Lexing",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1687/",
pages = "33204--33248",
ISBN = "979-8-89176-332-6",
abstract = "Identifying human morals and values embedded in language is essential to empirical studies of communication. However, researchers often face substantial difficulty navigating the diversity of theoretical frameworks and data available for their analysis. Here, we contribute MoVa, a well-documented suite of resources for generalizable classification of human morals and values, consisting of (1) 16 labeled datasets and benchmarking results from four theoretically-grounded frameworks; (2) a lightweight LLM prompting strategy that outperforms fine-tuned models across multiple domains and frameworks; and (3) a new application that helps evaluate psychological surveys. In practice, we specifically recommend a classification strategy, all@once, that scores all related concepts simultaneously, resembling the well-known multi-label classifier chain. The data and methods in MoVa can facilitate many fine-grained interpretations of human and machine communication, with potential implications for the alignment of machine behavior."
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<abstract>Identifying human morals and values embedded in language is essential to empirical studies of communication. However, researchers often face substantial difficulty navigating the diversity of theoretical frameworks and data available for their analysis. Here, we contribute MoVa, a well-documented suite of resources for generalizable classification of human morals and values, consisting of (1) 16 labeled datasets and benchmarking results from four theoretically-grounded frameworks; (2) a lightweight LLM prompting strategy that outperforms fine-tuned models across multiple domains and frameworks; and (3) a new application that helps evaluate psychological surveys. In practice, we specifically recommend a classification strategy, all@once, that scores all related concepts simultaneously, resembling the well-known multi-label classifier chain. The data and methods in MoVa can facilitate many fine-grained interpretations of human and machine communication, with potential implications for the alignment of machine behavior.</abstract>
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%0 Conference Proceedings
%T MoVa: Towards Generalizable Classification of Human Morals and Values
%A Chen, Ziyu
%A Sun, Junfei
%A Li, Chenxi
%A Nguyen, Tuan Dung
%A Yao, Jing
%A Yi, Xiaoyuan
%A Xie, Xing
%A Tan, Chenhao
%A Xie, Lexing
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F chen-etal-2025-mova
%X Identifying human morals and values embedded in language is essential to empirical studies of communication. However, researchers often face substantial difficulty navigating the diversity of theoretical frameworks and data available for their analysis. Here, we contribute MoVa, a well-documented suite of resources for generalizable classification of human morals and values, consisting of (1) 16 labeled datasets and benchmarking results from four theoretically-grounded frameworks; (2) a lightweight LLM prompting strategy that outperforms fine-tuned models across multiple domains and frameworks; and (3) a new application that helps evaluate psychological surveys. In practice, we specifically recommend a classification strategy, all@once, that scores all related concepts simultaneously, resembling the well-known multi-label classifier chain. The data and methods in MoVa can facilitate many fine-grained interpretations of human and machine communication, with potential implications for the alignment of machine behavior.
%U https://aclanthology.org/2025.emnlp-main.1687/
%P 33204-33248
Markdown (Informal)
[MoVa: Towards Generalizable Classification of Human Morals and Values](https://aclanthology.org/2025.emnlp-main.1687/) (Chen et al., EMNLP 2025)
ACL
- Ziyu Chen, Junfei Sun, Chenxi Li, Tuan Dung Nguyen, Jing Yao, Xiaoyuan Yi, Xing Xie, Chenhao Tan, and Lexing Xie. 2025. MoVa: Towards Generalizable Classification of Human Morals and Values. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 33204–33248, Suzhou, China. Association for Computational Linguistics.