TL;DR
DVMap introduces a fine-grained, demographic-based approach for aligning large language models with diverse human values, improving generalization across demographics and cultures.
Contribution
It presents a novel demographic-value mapping framework with a high-quality corpus, structured reasoning, and adaptive policy optimization for better pluralistic value alignment.
Findings
DVMap achieves 48.6% accuracy on cross-demographic tests.
It outperforms existing open-source models like DeepSeek-v3.2.
The approach demonstrates strong generalization and robustness.
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…
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