Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore ChatGPT's zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions, b) has meaningful emotion representations in terms of emotion categories and these affective dimensions, and c) can perform basic appraisal-based emotion elicitation of situations based on a prompt-based computational implementation of the OCC appraisal model. These findings are highly relevant: First, they show that the ability to solve complex affect processing tasks emerges from language-based token prediction trained on extensive data sets. Second, they show the potential of large language models for simulating, processing and analyzing human emotions, which has important implications for various applications such as sentiment analysis, socially interactive agents, and social robotics.

翻译：大规模语言模型，尤其是生成式预训练变换器（GPTs），在各类语言相关任务中展现出令人瞩目的成果。本文探究了ChatGPT仅通过提示（prompt）即可执行的零样本情感计算任务能力。我们证明ChatGPT能够：a)在效价（Valence）、唤醒度（Arousal）和支配度（Dominance）维度上执行有意义的情绪分析；b)基于情绪类别及其情感维度构建有效的情绪表征；c)依据OCC评价模型（OCC appraisal model）的提示驱动计算实现，对情境进行基础性评价驱动的情绪诱发。这些发现具有重要价值：其一，表明基于大规模数据集的语言标记预测训练能够涌现出解决复杂情感处理任务的能力；其二，揭示了大规模语言模型在模拟、处理和分析人类情绪方面的潜力，这对情感分析、社会交互代理及社交机器人等应用领域具有深远意义。