MoVa: Towards Generalizable Classification of Human Morals and Values

• URL: http://arxiv.org/abs/2509.24216v1
• Date: Mon, 29 Sep 2025 02:56:27 GMT
• Title: MoVa: Towards Generalizable Classification of Human Morals and Values
• Authors: Ziyu Chen, Junfei Sun, Chenxi Li, Tuan Dung Nguyen, Jing Yao, Xiaoyuan Yi, Xing Xie, Chenhao Tan, Lexing Xie,
• Abstract summary: MoVa is a well-documented suite of resources for generalizable classification of human morals and values.<n>The data and methods in MoVa can facilitate many fine-grained interpretations of human and machine communication.
• Score: 57.93595662296688
• License: http://creativecommons.org/licenses/by/4.0/
• 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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