Teaching Values to Machines: Simulating Human-Like Behavior in LLMs

Asaf Yehudai; Naama Rozen; Ariel Gera

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arXiv · 2605.30036

Teaching Values to Machines: Simulating Human-Like Behavior in LLMs

Asaf Yehudai·Naama Rozen·Ariel Gera

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Abstract

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.

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Asaf Yehudai, Naama Rozen, Ariel Gera. 2026-05-28. Teaching Values to Machines: Simulating Human-Like Behavior in LLMs. https://arxiv.org/abs/2605.30036

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