MoVa: Towards Generalizable Classification of Human Morals and Values
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911182577729536 |
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| author | Chen, Ziyu Sun, Junfei Li, Chenxi Nguyen, Tuan Dung Yao, Jing Yi, Xiaoyuan Xie, Xing Tan, Chenhao Xie, Lexing |
| author_facet | Chen, Ziyu Sun, Junfei Li, Chenxi Nguyen, Tuan Dung Yao, Jing Yi, Xiaoyuan Xie, Xing Tan, Chenhao Xie, Lexing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoVa: Towards Generalizable Classification of Human Morals and Values Chen, Ziyu Sun, Junfei Li, Chenxi Nguyen, Tuan Dung Yao, Jing Yi, Xiaoyuan Xie, Xing Tan, Chenhao Xie, Lexing Computation and Language Computers and Society 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. |
| title | MoVa: Towards Generalizable Classification of Human Morals and Values |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2509.24216 |