大型语言模型知道公众人物的个性是如何被公众所感知的
1Stanford University, Stanford, USA. xcao@stanford.edu.
Scientific reports
|March 21, 2024
概括
我们可以准确地预测公众人物的个性认知,使用他们的名字的位置在GPT-3的语义空间. 这表明语言模型能够捕捉到细微的个体差异和个性特征.
科学领域:
- 计算语言学计算语言学
- 心理学 心理学 心理学
- 人工智能的人工智能是人工智能.
背景情况:
- 了解人格感知在社会科学中至关重要.
- 评估大型语言模型 (LLM) 在捕捉类似人类判断方面的能力是一个新兴的研究领域.
- 之前的研究已经探索了各种语言任务的语义空间,但它们预测人格认知的潜力不太了解.
研究的目的:
- 调查GPT-3的语义空间是否可以预测人类对公众人物个性的看法.
- 量化这些预测的准确性.
- 探索GPT-3词嵌入所捕获的个性信号的性质.
主要方法:
- 从600名人类评分员中收集了226名公众人物的"五大人物"人格认知.
- 从GPT-3.0中提取了这些公众人物的名字嵌入.
- 利用交叉验证的线性回归来预测人类从名称嵌入的感知.
- 在二次分析中对可喜和人口统计变量进行控制.
主要成果:
- 从GPT-3名称嵌入中准确预测人格感知 (r = .78.88没有控制).
- 即使控制了可爱性和人口统计数据 (r = .53.70),预测准确度仍然很高.
- 预测模型表现出高度的面部有效性,极端形容词与预期的性格特征保持一致.
结论:
- GPT-3的语义空间有效地捕捉了与个人差异和个性特征相关的信号.
- LLM可以作为一种工具来理解和预测人类对人格的判断.
- 这些发现突显了计算方法在心理学研究中的潜力.
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