Values Are Not Single Labels: Distributional Value Profiling Across Groups and Contexts

Human values are not single labels, but distributions: they vary across individuals, contexts, and their relationships with other values. Yet most alignment approaches still compress group-level values into scalar scores or single representative targets, erasing within-group diversity and missing how value expression shifts across situations. We introduce Distributional Value Profiling, a framework for representing group-level value expression as distributions over expression modes, intensity, and inter-value relationships. Using large-scale text corpora spanning cultural, political, and religious groups, we construct profiles that capture not only which values are expressed, but also how they are expressed, how strongly they are emphasized, and how they relate to one another. We then model context-dependent shifts in these profiles using optimal transport, revealing structured redistributions of value expression across situations. Finally, we show that these profiles are actionable: a lightweight profiling model generalizes to unseen value-context combinations, and an activation-based steering method guides model outputs toward target distributions at inference time, consistently outperforming baselines across diverse benchmarks. Together, these results establish distributional value profiling as a concrete step toward pluralistic alignment, treating within-group diversity not as noise to be averaged away, but as structure to be measured, predicted, and controlled.