用心理学理论让大模型表现出类人的价值观。
Teaching Values to Machines: Simulating Human-Like Behavior in LLMs
- 基于心理价值理论引导大模型生成类人行为。
- 500万次测试显示大模型与人类价值高度一致。
- 适合用于心理模拟与人机交互研究。
大型语言模型（LLMs）展现出扮演不同人格和角色的惊人能力，但其是否能表现出连贯、类人的价值体系仍不明确。本文借鉴成熟的心理学价值理论，诱导大模型形成类人价值观，并评估其与人类研究中观察到的模式的一致性。通过使用经过验证的心理学量表，我们开展了大规模实验——超过500万道问题——以评估领先LLMs中的价值结构及其与行为的关系，并与人类进行对比。结果表明，经过价值引导的大模型在两个维度上均与人类表现出强一致性。此外，引入人类价值分布可显著提升基于价值诱导的LLM在群体层面的行为模拟效果。这些发现凸显了价值诱导型大模型作为心理基础坚实的人类行为模拟工具的巨大潜力。
原文摘要 · Abstract (English)
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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