基于LLM的数字孪生的心理测量可比性
Psychometric Comparability of LLM-Based Digital Twins
AI总结：
提出构念效度框架，评估LLM数字孪生在聚合、项目、网络和决策层面的心理测量可比性，发现其在高聚合准确性和网络结构上接近人类，但在项目级相关、启发式偏差和时变敏感性上存在差异。
摘要：
大型语言模型（LLM）作为人类受访者的数字孪生，但其心理测量可比性仍不确定。我们提出了一个涵盖构念表征和法则跨度（nomothetic span）的构念效度框架，以人类金标准为基准对模型进行对比。在多项研究中，数字孪生实现了高聚合级准确性和剖面相关性，但项目级相关性减弱。在词汇联想测试中，LLM网络表现出类人的小世界结构和理论一致的社区，但在词汇和局部结构上存在差异。在决策和情境化任务中，它们未能充分再现启发式偏差，表现出规范性理性、压缩方差和有限的时变敏感性。丰富特征和特质相关的条件改善了五大人格预测和法则跨度对齐，但网络不变性仍然有限，存在部分配置解和持续的载荷差异。在英语和中文的跨语言自由文本任务中，丰富特征的数字孪生更好地逼近了构念层面的叙事内容，但语言和个体差异仍然存在。这些发现表明，数字孪生在经过验证的边界内最为有用，即构念、任务和推断水平与人类数据证据一致。
英文摘要：
Large language models (LLMs) act as digital twins for human respondents, yet their psychometric comparability remains uncertain. We propose a construct validity framework spanning construct representation and the nomothetic span, benchmarking models against human gold standards. Across studies, digital twins achieved high aggregate-level accuracy and profile correlations, but showed attenuated item-level correlations. In word association tests, LLM networks exhibited humanlike small-world structure and theory-consistent communities, yet diverged lexically and in local structure. In decision-making and contextualized tasks, they under-reproduced heuristic biases, demonstrating normative rationality, compressed variance, and limited temporal sensitivity. Feature-rich and trait relevant conditioning improved Big Five personality prediction and nomothetic-span alignment, but network invariance remained limited, with partial configural solutions and persistent loading differences. In cross-language free-text tasks in English and Chinese, feature-rich digital twins better approximated construct-level narrative content, but linguistic and idiographic differences persisted. These findings clarify that digital twins are most useful within validated boundaries, where the construct, task and level of inference align with evidence from human data.
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