How social context shapes value-related content in Large Language Model outputs

Abstract

We study how reliably value-related content in Large Language Model (LLM) outputs can be recovered by another model, and how that recoverability depends on the context in which the output is generated. To this end, we introduce the Generator-Inquisitor framework, in which one model generates text conditioned on a target value and another model infers the underlying value from that text. Using Schwartz's four value dimensions—Openness to Change, Conservation, Self-Enhancement, and Self-Transcendence—instantiated across Fiske's four relational contexts—Communal Sharing, Equality Matching, Authority Ranking, and Market Pricing—we evaluate six state-of-the-art LLMs across seven experiments. Value recovery accuracy is generally high but varies systematically across value dimensions and relational contexts, with Self-Enhancement being the hardest to recover, especially in the Communal Sharing context. Cross-model evaluation showed negligible decrease in accuracy when Generator and Inquisitor were from two different model families, and a perturbation experiment confirmed that recoverability relies on value-relevant content rather than superficial lexical cues. A preliminary annotation study further showed that the dominant source of misclassification errors is the Inquisitor rather than the Generator. Together, these results show that the recoverability of value-related content in LLM outputs varies systematically with relational context. Like for humans, this observation calls for a contextualized approach to the study of value alignment in machines.