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This paper investigates whether LLMs can exhibit human-like value structures by prompting them with values derived from established psychological value theory and comparing their responses to validated questionnaires against human data. They conducted large-scale experiments, querying LLMs over 5 million times, to evaluate value structures and value-behavior relationships. Results show strong agreement between value-prompted LLMs and humans, suggesting LLMs can be used for psychologically grounded simulations of human behavior.
LLMs can be made to think more like us: prompting them with human values yields behavior surprisingly aligned with real-world psychological patterns.
Large Language Models (LLMs) demonstrate a remarkable capacity to adopt different personas and roles; however, it remains unclear whether they can manifest behavior that adheres to a coherent, human-like value structure. In this work, we draw on established psychological value theory to induce human-like values in LLMs and assess their alignment with patterns observed in human studies. Using validated psychological questionnaires, we conduct large-scale experiments -- over 5 million questions -- to evaluate value structures and value-behavior relationships in leading LLMs and compare them to humans. Our findings reveal strong agreement between value-prompted LLMs and humans across both dimensions. Moreover, incorporating human value distributions enhances population-level simulations with value-induced LLMs. These findings highlight the potential of value-induced LLMs as effective, psychologically grounded tools for simulating human behavior.
