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大型语言模型和人类在判断公众人物性格方面趋于一致。

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

作者信息

Cao Xubo, Kosinski Michal

机构信息

Graduate School of Business, Stanford University, Stanford, CA 94305, USA.

出版信息

PNAS Nexus. 2024 Sep 19;3(10):pgae418. doi: 10.1093/pnasnexus/pgae418. eCollection 2024 Oct.

Abstract

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 = 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.

摘要

ChatGPT-4和600名人类评分者使用十项人格量表对226位公众人物的性格进行了评估。ChatGPT-4与人类总体评分之间的相关性在0.76至0.87之间,优于专门训练用于进行此类预测的模型。值得注意的是,该模型没有获得任何关于其性能的训练数据或反馈。我们讨论了ChatGPT-4准确模仿人类反应能力的潜在解释和实际意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e02/11443023/19188551aebc/pgae418f1.jpg

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