Beyond Isolated Cultures: Cultural-Sphere Priors Improve LLM Alignment and Narrow the Low-Resource Gap

Large language models often skew cultural values toward Western and English-speaking populations, while existing alignment methods treat each culture independently. We ask whether cultural-sphere structure can serve as a prior for alignment, using the Inglehart–Welzel spheres to relate cultures with similar value profiles. Across 10 models and 3 benchmarks (WVS, VSM, and open-ended PRISM), sphere-aware prompting consistently outperforms culture-only baselines, with a mean relative gain of +12.1% on WVS and particularly large gains for low-resource cultures. To identify the source of improvement, we ablate the prior and trace the gain to within-sphere consensus; the prior also recovers more human-like cultural geometry and reduces low-resource drift toward dominant spheres. We further show that fine-tuning on same-sphere consensus can internalize the sphere prior for cultures that lack training data, producing alignment that rivals an oracle trained on held-out labels and generalizes beyond the training distribution. Overall, our results support cultural-sphere relations as a useful prior for alignment across both prompting and fine-tuning.