Large Language Models (LLMs) are transforming diverse fields and gaining
increasing influence as human proxies. This development underscores the urgent
need for evaluating value orientations and understanding of LLMs to ensure
their responsible integration into public-facing applications. This work
introduces ValueBench, the first comprehensive psychometric benchmark for
evaluating value orientations and value understanding in LLMs. ValueBench
collects data from 44 established psychometric inventories, encompassing 453
multifaceted value dimensions. We propose an evaluation pipeline grounded in
realistic human-AI interactions to probe value orientations, along with novel
tasks for evaluating value understanding in an open-ended value space. With
extensive experiments conducted on six representative LLMs, we unveil their
shared and distinctive value orientations and exhibit their ability to
approximate expert conclusions in value-related extraction and generation
tasks. ValueBench is openly accessible at
this https URL

介绍了 ValueBench 作为第一个综合性的心理测量基准，用于评估大型语言模型的价值取向和价值理解，并通过在人工智能与人类的实际互动中进行评估管道和开放性价值空间任务的实验，揭示了六个代表性大型语言模型的共同和独特的价值取向，展示了它们在价值相关的提取和生成任务中接近专家结论的能力。