Human values and their measurement are long-standing interdisciplinary inquiry. Recent advances in AI have sparked renewed interest in this area, with large language models (LLMs) emerging as both tools and subjects of value measurement. This work introduces Generative Psychometrics for Values (GPV), an LLM-based, data-driven value measurement paradigm, theoretically grounded in text-revealed selective perceptions. The core idea is to dynamically parse unstructured texts into perceptions akin to static stimuli in traditional psychometrics, measure the value orientations they reveal, and aggregate the results. Applying GPV to human-authored blogs, we demonstrate its stability, validity, and superiority over prior psychological tools. Then, extending GPV to LLM value measurement, we advance the current art with 1) a psychometric methodology that measures LLM values based on their scalable and free-form outputs, enabling context-specific measurement; 2) a comparative analysis of measurement paradigms, indicating response biases of prior methods; and 3) an attempt to bridge LLM values and their safety, revealing the predictive power of different value systems and the impacts of various values on LLM safety. Through interdisciplinary efforts, we aim to leverage AI for next-generation psychometrics and psychometrics for value-aligned AI.

翻译：人类价值观及其测量是一个长期存在的跨学科研究课题。近期人工智能的进展重新激发了该领域的兴趣，大语言模型（LLMs）既作为价值观测量的工具，也成为了测量对象。本研究提出了"价值观生成式心理测量学"（Generative Psychometrics for Values, GPV），这是一种基于LLM的数据驱动价值观测量范式，其理论基础是文本揭示的选择性感知。其核心思想是将非结构化文本动态解析为类似于传统心理测量中静态刺激物的感知单元，测量这些感知所揭示的价值取向，并对结果进行聚合分析。将GPV应用于人类撰写的博客数据，我们证明了该方法的稳定性、效度及其相对于传统心理学工具的优越性。随后，我们将GPV扩展至LLM价值观测量领域，通过以下三方面推进了当前研究：1）提出一种基于LLM可扩展自由形式输出的心理测量方法，支持情境化测量；2）对不同测量范式进行比较分析，揭示了现有方法存在的响应偏差；3）尝试建立LLM价值观与其安全性之间的关联，揭示了不同价值体系的预测能力以及各类价值观对LLM安全性的影响。通过跨学科努力，我们旨在利用人工智能推动新一代心理测量学发展，同时运用心理测量学促进价值对齐的人工智能系统构建。