使用大型语言模型来估计推理中的信念强度.
Jérémie Beucler1, Zoe Purcell2, Lucie Charles3
1LaPsyDÉ, CNRS, Université Paris-Cité, 46, rue Saint-Jacques, F-75005, Paris, France. jeremie.beucler@gmail.com.
Behavior research methods
|January 20, 2026
概括
我们使用大型语言模型 (LLM) 开发了一种自动化方法,用于测量认知任务中的信念强度. 这个工具量化了刻板印象驱动的信念,增强了对启发式和偏见的研究.
科学领域:
- 认知心理学 认知心理学
- 计算社会科学 计算社会科学
- 人工智能的人工智能
背景情况:
- 在启发式和偏见任务中量化信念的强度在方法上是具有挑战性的.
- 现有的方法与系统的测量和操纵信念强度作斗争.
- 基本率忽视任务,如律师-工程师问题,突出刻板印象和统计信息之间的冲突.
研究的目的:
- 通过使用大型语言模型 (LLM) 引入一种自动化方法来测量和操纵信念的强度.
- 为了创建一个全面的,开放访问的数据库,在刻板印象驱动的信念强度上有所不同.
- 验证LLM衍生的信念强度测量,并证明其在认知研究中的实用性.
主要方法:
- 利用大型语言模型 (LLM) 来系统地测量和操纵信念强度.
- 为"律师-工程师"基本率疏忽任务开发了超过10万个独特的项目.
- 创建一个开放访问的数据库和一个R包 (baserater) 可访问性.
主要成果:
- 来自LLM的信念强度指标显示与人类典型性评级有很强的相关性.
- 该测量有力地预测了人类在基准率疏忽任务中的选择.
- 在现有研究项目中确定了刻板印象驱动的信念强度的显著,以前未被注意到的变化.
结论:
- 基于LLM的方法提供了一种强大,可扩展和精确的方法来量化信念的强度.
- 创建的数据库和R包促进了严格的,可复制的认知研究.
- 方法的改进和跨文化适应是可能的,推进认知和计算建模.
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