评估通用大型语言模型在识别人类面部情绪方面的表现
Benjamin W Nelson1,2, Ari Winbush3, Steven Siddals4
1Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. bnelson9@bidmc.harvard.edu.
NPJ digital medicine
|October 16, 2025
概括
领先的大型语言模型 (LLM),如GPT-4o和Gemini显示出强大的人类面部表情识别,与NimStim数据集上的人类准确度相匹配或超过,具有潜在的医疗保健用途.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 心理学 心理学 心理学
背景情况:
- 人类面部表情识别对于社会互动至关重要.
- 评估人工智能 (AI) 在理解人类情绪方面的能力是一个新兴的研究领域.
- 大型语言模型 (LLM) 正在越来越多地展示先进的认知能力.
研究的目的:
- 评估领先的LLMs的面部表情识别能力.
- 使用标准化数据集,将LLM的表现与人类的准确性进行比较.
- 探索LLM在社会情感理解和医疗保健方面的潜力.
主要方法:
- 利用了NimStim数据集,这是一个广泛认可的面部表情刺激的基准.
- 测试了三个著名的LLM:GPT-4o,双子座2.0实验和克劳德3.5索内特.
- 量化评估模型性能与地面真相相对比人类标签.
主要成果:
- GPT-4o和双子座2.0实验证明了与人类水平相比或超过的性能.
- 模型在识别"平静/中立"和"惊喜"表达式方面表现特别强.
- 在模型中观察到"恐惧"表达式的错误分类趋势.
- 在LLM预测和基本真相之间普遍存在很高的一致性.
结论:
- 法学士表现出显著和不断增长的社会情感能力.
- 在面部表情识别中LLM的准确性表明在医疗保健中的潜在应用,例如诊断支持或患者监测.
- 需要进一步的研究来完善情绪识别,特别是对于复杂或微妙的表达方式,如恐惧.
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