Large language models and humans converge in judging public figures' personalities

被引：0
|
作者：
Cao, Xubo [1 ]
Kosinski, Michal [1 ]
机构：
[1] Stanford Univ, Grad Sch Business, Stanford, CA 94305 USA
来源：
PNAS NEXUS | 2024年 / 3卷 / 10期
关键词：
personality perception; zero-shot predictions; large language models; AI;
D O I：
10.1093/pnasnexus/pgae418
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
ChatGPT-4 and 600 human raters evaluated 226 public figures' personalities using the Ten-Item Personality Inventory. The correlation between ChatGPT-4 and aggregate human ratings ranged from r = 0.76 to 0.87, outperforming the models specifically trained to make such predictions. Notably, the model was not provided with any training data or feedback on its performance. We discuss the potential explanations and practical implications of ChatGPT-4's ability to mimic human responses accurately.
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