Value-Action Alignment in Large Language Models under Privacy-Prosocial Conflict

• URL: http://arxiv.org/abs/2601.03546v1
• Date: Wed, 07 Jan 2026 03:30:42 GMT
• Title: Value-Action Alignment in Large Language Models under Privacy-Prosocial Conflict
• Authors: Guanyu Chen, Chenxiao Yu, Xiyang Hu,
• Abstract summary: We introduce a context-based assessment protocol that administers standardized questionnaires for privacy attitudes, prosocialness, and acceptance of data sharing.<n>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.
• Score: 5.739904853130176
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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