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DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping

Year: 2026
Type: conference-paper
Abstract: Current Large Language Models (LLMs) typically rely on coarse-grained national labels for pluralistic value alignment.However, such macro-level supervision often obscures intra-country value heterogeneity, yielding a loose alignment.We argue that resolving this limitation requires shifting from national labels to multi-dimensional demographic constraints, which can identify groups with predictable, high-consensus value preference.To this end, we propose DVMap (High-Consensus Demographic-Value Mapping), a framework for fine-grained pluralistic value alignment.In this framework, we first present a demographic archetype extraction strategy to construct a high-quality value alignment corpus of 56,152 samples from the World Values Survey (WVS) by strictly retaining respondents with consistent value preferences under identical demographics.Over this corpus, we introduce a Structured Chain-of-Thought (CoT) mechanism that explicitly guides LLMs to reason about demographic-value correlations.Subsequently, we employ Group Relative Policy Optimization (GRPO) to achieve adaptive anchoring of value distributions.To rigorously evaluate generalization, we further establish a triple-generalization benchmark (spanning cross-demographic, cross-country, and crossvalue) comprising 21,553 samples.Experimental results demonstrate that DVMap effectively learns the manifold mapping from demographics to values, exhibiting strong generalization and robustness.On cross-demographic tests, Qwen3-8B-DVMap achieves 48.6% accuracy, surpassing the advanced open-source LLM DeepSeek-v3.2(45.1%).The source code and dataset are available at https://github. com/EnlightenedAI/DVMap.
DOI: 10.18653/v1/2026.acl-long.909
Institution: Tianjin University
Language: English
Subfield: Safety Research
Open Access status: hybrid
OpenAlex ID: W7166807691
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