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Language and Cognition
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一个基于大型语言模型的语义嵌入空间,用于建模人类信仰.
Byunghwee Lee1, Rachith Aiyappa1, Yong-Yeol Ahn1
1Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
Nature human behaviour
|June 4, 2025
概括
研究人员利用大型语言模型和在线辩论数据绘制了成千上万种信仰的地图. 这揭示了信仰如何相互连接,预测新的信仰,估计认知不和,提供了对信仰形成的见解.
科学领域:
- 认知科学 认知科学
- 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:
- 信念是人类认知,决策和社会互动的基础.
- 了解信念的相互关系是关键,但先前的研究在范围和方法上是有限的.
- 现有的研究往往侧重于特定问题,并依赖于传统的调查方法.
研究的目的:
- 开发一种新的方法来研究成千上万种信仰的相互作用.
- 利用在线用户辩论数据和大型语言模型进行信念分析.
- 将信念映射到神经嵌入空间中,以便进行细致的调查.
主要方法:
- 利用一个微调的大型语言模型来构建一个神经嵌入空间的信念.
- 分析了广泛的在线用户辩论数据,以捕捉信仰的相互关系.
- 将各种信仰映射到构建的嵌入空间上,以表示它们的连接和两极分化.
主要成果:
- 信念空间有效地捕捉了各种社会问题的相互联系和信仰的两极分化.
- 信念空间中的位置可以预测个体的新信仰.
- 该模型可以根据现有和新信念之间的空间距离来估计认知失调.
结论:
- 大型语言模型与集体在线信念数据相结合,为信念形成提供了强大的洞察力.
- 这种方法为研究复杂的信念系统提供了一个可扩展和细微的方法.
- 这些发现有助于理解支配人类信仰结构和动态的基本原则.

