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        <title>Value–Action Alignment in Large Language Models under Privacy–Prosocial Conflict</title>
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    <abstract>Large language models (LLMs) are increasingly used to simulate decision-making tasks involving personal data sharing, where privacy concerns and prosocial motivations can push choices in opposite directions. Existing evaluations often measure privacy-related attitudes or sharing intentions in isolation, which makes it difficult to determine whether a model’s expressed values jointly predict its downstream data-sharing actions as in real human behaviors. We introduce a context-based assessment protocol that sequentially administers standardized questionnaires for privacy attitudes, prosocialness, and acceptance of data sharing within a bounded, history-carrying session. To evaluate value-action alignments under competing attitudes, we use multi-group structural equation modeling (MGSEM) to identify relations from privacy concerns and prosocialness to data sharing. We propose Value-Action Alignment Rate (VAAR), a human-referenced directional agreement metric that aggregates path-level evidence for expected signs. Across multiple LLMs, we observe stable but model-specific Privacy-PSA-AoDS profiles, and substantial heterogeneity in value-action alignment.</abstract>
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%0 Conference Proceedings
%T Value–Action Alignment in Large Language Models under Privacy–Prosocial Conflict
%A Chen, Guanyu
%A Yu, Chenxiao
%A Hu, Xiyang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-value
%X Large language models (LLMs) are increasingly used to simulate decision-making tasks involving personal data sharing, where privacy concerns and prosocial motivations can push choices in opposite directions. Existing evaluations often measure privacy-related attitudes or sharing intentions in isolation, which makes it difficult to determine whether a model’s expressed values jointly predict its downstream data-sharing actions as in real human behaviors. We introduce a context-based assessment protocol that sequentially administers standardized questionnaires for privacy attitudes, prosocialness, and acceptance of data sharing within a bounded, history-carrying session. To evaluate value-action alignments under competing attitudes, we use multi-group structural equation modeling (MGSEM) to identify relations from privacy concerns and prosocialness to data sharing. We propose Value-Action Alignment Rate (VAAR), a human-referenced directional agreement metric that aggregates path-level evidence for expected signs. Across multiple LLMs, we observe stable but model-specific Privacy-PSA-AoDS profiles, and substantial heterogeneity in value-action alignment.
%U https://aclanthology.org/2026.findings-acl.2065/
%P 41545-41564

Markdown (Informal)

[Value–Action Alignment in Large Language Models under Privacy–Prosocial Conflict](https://aclanthology.org/2026.findings-acl.2065/) (Chen et al., Findings 2026)

• Value–Action Alignment in Large Language Models under Privacy–Prosocial Conflict (Chen et al., Findings 2026)

ACL

• Guanyu Chen, Chenxiao Yu, and Xiyang Hu. 2026. Value–Action Alignment in Large Language Models under Privacy–Prosocial Conflict. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41545–41564, San Diego, California, United States. Association for Computational Linguistics.