基于大型语言模型的生成心理测量，衡量人类和人工智能价值观

Measuring Human and AI Values based on Generative Psychometrics with Large Language Models

September 18, 2024

作者: Haoran Ye, Yuhang Xie, Yuanyi Ren, Hanjun Fang, Xin Zhang, Guojie Song

cs.AI

摘要

人类价值观及其衡量是一个长期的跨学科探讨课题。人工智能的最新进展引发了对这一领域的新一轮兴趣，大型语言模型（LLMs）作为价值衡量的工具和对象崭露头角。本研究引入了基于生成心理测量的价值观（GPV），这是一个基于LLM的数据驱动价值观测量范式，理论基础是文本揭示的选择性认知。我们首先对LLM进行微调，以实现准确的感知级别价值衡量，并验证LLMs将文本解析为认知的能力，构成GPV管道的核心。将GPV应用于人类撰写的博客，我们展示了其稳定性、有效性，并证明其优于先前的心理学工具。然后，将GPV扩展到LLM价值测量，我们通过以下方式推进了当前技术：1）一种心理测量方法，根据LLM的可扩展和自由形式输出来衡量LLM的价值，实现了特定上下文的测量；2）对测量范式进行比较分析，指出了先前方法的响应偏差；3）尝试将LLM的价值与安全性联系起来，揭示了不同价值体系的预测能力以及各种价值对LLM安全性的影响。通过跨学科的努力，我们旨在利用人工智能实现下一代心理测量，并利用心理测量实现与价值一致的人工智能。

English

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. We begin by fine-tuning an LLM for accurate perception-level value measurement and verifying the capability of LLMs to parse texts into perceptions, forming the core of the GPV pipeline. 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.