在多式大型语言模型中评估跨种族情绪识别能力,使用阅读眼睛中的思维测试
Elad Refoua1, Zohar Elyoseph2,3, David Piterman4
1Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel. eladrefoua@gmail.com.
Scientific reports
|February 20, 2026
概括
聊天GPT-4o显示了先进的情感识别,在眼部情感测试中超过了不同种族群体的人类准确性. 这突显了社会认知任务的多式大型语言模型 (MLLMs) 的重大进展.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 准确的情感识别对于社会认知至关重要,但容易受到人类偏见的影响.
- 多模式大语言模型 (MLLMs) 提供了客观情感分析的潜力.
- 之前的MLLM评估主要使用了种族一致的数据集.
研究的目的:
- 系统地评估领先的MLLM对跨种族情绪识别准确性和一致性的评估.
- 为了比较MLLM的表现与偶然和既定的人类规范数据.
- 评估ChatGPT-4,ChatGPT-4o和Claude 3 Opus在眼睛中阅读思维测试 (RMET) 的能力.
主要方法:
- 评估了三个主要的MLLM (ChatGPT-4,ChatGPT-4o,Claude 3 Opus) 的情况.
- 使用三个RMET版本评估表现,其中包括白色,黑色和韩国面部刺激.
- 对准确性进行了分析,并与人类规范数据进行了比较.
主要成果:
- 在所有族群中,ChatGPT-4o显示出明显高于偶然的准确性 (p < .001).
- 聊天GPT-4o实现了高精度 (83.3%-94.4%),超过了人类平均表现,并显示了跨种族群体的一致性.
- 聊天GPT-4的表现接近人类的平均水平,而Claude 3 Opus的表现接近机会水平.
结论:
- 与其他评估的MLLM相比,ChatGPT-4o表现出优越且一致的跨种族情绪识别能力.
- 这项研究强调了MLLM在社会认知任务中的快速进步.
- 由于模型性能差距和任务限制,需要进一步验证和伦理考虑.
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