There has been concern about ideological basis and possible discrimination in text generated by Large Language Models (LLMs). We test possible value biases in ChatGPT using a psychological value theory. We designed a simple experiment in which we used a number of different probes derived from the Schwartz basic value theory (items from the revised Portrait Value Questionnaire, the value type definitions, value names). We prompted ChatGPT via the OpenAI API repeatedly to generate text and then analyzed the generated corpus for value content with a theory-driven value dictionary using a bag of words approach. Overall, we found little evidence of explicit value bias. The results showed sufficient construct and discriminant validity for the generated text in line with the theoretical predictions of the psychological model, which suggests that the value content was carried through into the outputs with high fidelity. We saw some merging of socially oriented values, which may suggest that these values are less clearly differentiated at a linguistic level or alternatively, this mixing may reflect underlying universal human motivations. We outline some possible applications of our findings for both applications of ChatGPT for corporate usage and policy making as well as future research avenues. We also highlight possible implications of this relatively high-fidelity replication of motivational content using a linguistic model for the theorizing about human values.

翻译：大型语言模型（LLMs）生成的文本是否存在意识形态基础与潜在歧视的问题已引发学界关注。本研究采用心理学价值理论，对ChatGPT可能存在的价值偏差进行检验。我们设计了一个简易实验：基于施瓦茨基础价值理论（包括修订版肖像价值问卷的条目、价值类型定义及价值名称），构建多种探测方式。通过OpenAI API反复向ChatGPT发送提示词生成文本，并运用理论驱动的价值词典与词袋方法对生成语料进行价值内容分析。总体而言，未发现显著显性价值偏差的证据。实验结果显示出充分的构念效度与区分效度，生成的文本符合心理学模型的理论预期，表明价值内容得以高保真度地传递至输出结果。部分社会导向型价值存在融合现象，这或许说明此类价值在语言层面区分度较低，亦可能映射出人类共有的深层动机。本研究为ChatGPT在企业应用与政策制定中的实践场景，以及未来研究方向提供了潜在应用路径。同时，我们着重指出，语言模型对动机性内容的高保真复现，对深化人类价值理论建构具有重要启示意义。