People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe
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Abstract (arXiv)
As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of values becomes a pressing concern. A growing literature has leveraged large scale surveys to investigate to what extent LLMs' and humans' stated values and opinions align. With limited exceptions, studied populations have been defined country borders or cultural bounds. Yet, this focus neglects the role that socio-demographic divides may play for value alignment disparities. Relying on the European Social Survey, we address this knowledge gap by considering value alignment displayed with respect to 10 prominent commercial LLMs in terms of 15 socio-demographic variables as well as country of residence. Our analyses reveal that LLMs are indeed unequally aligned to the values of different socio-demographic groups, notably those defined by education, income, occupation and religion. When examining alignment at the individual level, a respondent's country, taken as a stand-alone variable, explains a substantial amount of variation that is on par with the full set of considered socio-demographics. Further disentangling the respective role of country-level and socio-demographic factors, we find they are complementary in explaining value alignment patterns, with their relative weights varying across the subset of questions considered.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 7 Aug 2026.
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17 of 44 references.
References · 17
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5 more
- 2025Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMscited by 8
- 2026Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMscited by 4
- 2026Bias Beyond Borders: Political Ideology Evaluation and Steering in Multilingual LLMscited by 4
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- 2026PLURAL: A Global Dataset for Value Alignment