Supplementary data for the article: 'The use of ChatGPT for personality research: Administering questionnaires using generated personas'

Personality research has traditionally relied on questionnaires, which bring with them inherent limitations, such as response style bias. With the emergence of large language models such as ChatGPT, the question arises as to what extent these models can be used in personality research. In this study, ChatGPT (GPT-4) generated 2000 text-based personas. Next, for each persona, ChatGPT completed a short form of the Big Five Inventory (BFI-10), the Brief Sensation Seeking Scale (BSSS), and a Short Dark Triad (SD3). The mean scores on the BFI-10 items were found to correlate strongly with means from previously published research, and principal component analysis revealed a clear five-component structure. Certain relationships between traits, such as a negative correlation between the age of the persona and the BSSS score, were clearly interpretable, while some other correlations diverged from the literature. An additional analysis using four new sets of 2000 personas each, including a set of ‘realistic’ personas and a set of cinematic personas, showed that the correlation matrix among personality constructs was affected by the persona set. It is concluded that evaluating questionnaires and research hypotheses prior to engaging with real individuals holds promise.

人格研究传统上多依赖自陈问卷法，但此类方法存在固有局限，例如作答风格偏差（response style bias）。随着ChatGPT等大语言模型（Large Language Model，LLM）的问世，一个核心问题随之浮现：这类模型在人格研究中能够发挥多大效用？本研究中，ChatGPT（GPT-4）生成了2000份基于文本的虚拟人格样本。随后，针对每份虚拟人格样本，ChatGPT完成了大五人格量表简化版（Big Five Inventory-10, BFI-10）、简易感觉寻求量表（Brief Sensation Seeking Scale, BSSS）以及短版黑暗三人格量表（Short Dark Triad, SD3）的作答。研究发现，大五人格量表简化版（BFI-10）各条目得分的均值与既往已发表研究中的对应均值呈强相关，且主成分分析结果呈现出清晰的五成分结构。人格特质间的部分关联具备明确的可解释性，例如虚拟人格样本的年龄与简易感觉寻求量表（BSSS）得分呈负相关，但另有部分关联结果与现有文献不符。后续额外开展的分析中，研究人员又生成了4组各含2000份虚拟人格样本的数据，其中包含‘写实型’虚拟人格样本与‘影视型’虚拟人格样本，结果显示人格特质结构间的相关矩阵会因虚拟人格样本集的不同而发生变化。综上，在招募真实人类受试者开展研究前，先对问卷及研究假设进行预评估具备可观的应用前景。