基于基础模型的类人情感认知

Human-like Affective Cognition in Foundation Models

September 18, 2024

作者: Kanishk Gandhi, Zoe Lynch, Jan-Philipp Fränken, Kayla Patterson, Sharon Wambu, Tobias Gerstenberg, Desmond C. Ong, Noah D. Goodman

cs.AI

摘要

理解情绪对人类互动和体验至关重要。人类很容易从情境或面部表情中推断情绪，从情绪中推断情境，并进行各种其他情感认知。现代人工智能在这些推断方面表现如何？我们引入了一个评估框架，用于测试基础模型中的情感认知能力。从心理学理论出发，我们生成了1,280个多样化情境，探索评估、情绪、表情和结果之间的关系。我们评估了基础模型（GPT-4、Claude-3、Gemini-1.5-Pro）和人类（N = 567）在精心选择的条件下的能力。我们的结果显示，基础模型往往与人类直觉一致，匹配或超过参与者间的一致性。在某些条件下，模型表现“超人类”——它们比平均人类更好地预测模态人类的判断。所有模型都受益于思维链推理。这表明基础模型已经获得了类似人类的对情绪及其对信念和行为的影响的理解。

English

Understanding emotions is fundamental to human interaction and experience. Humans easily infer emotions from situations or facial expressions, situations from emotions, and do a variety of other affective cognition. How adept is modern AI at these inferences? We introduce an evaluation framework for testing affective cognition in foundation models. Starting from psychological theory, we generate 1,280 diverse scenarios exploring relationships between appraisals, emotions, expressions, and outcomes. We evaluate the abilities of foundation models (GPT-4, Claude-3, Gemini-1.5-Pro) and humans (N = 567) across carefully selected conditions. Our results show foundation models tend to agree with human intuitions, matching or exceeding interparticipant agreement. In some conditions, models are ``superhuman'' -- they better predict modal human judgements than the average human. All models benefit from chain-of-thought reasoning. This suggests foundation models have acquired a human-like understanding of emotions and their influence on beliefs and behavior.