How humans infer discrete emotions is a fundamental research question in the field of psychology. While conceptual knowledge about emotions (emotion knowledge) has been suggested to be essential for emotion inference, evidence to date is mostly indirect and inconclusive. As the large language models (LLMs) have been shown to support effective representations of various human conceptual knowledge, the present study further employed artificial neurons in LLMs to investigate the mechanism of human emotion inference. With artificial neurons activated by prompts, the LLM (RoBERTa) demonstrated a similar conceptual structure of 27 discrete emotions as that of human behaviors. Furthermore, the LLM-based conceptual structure revealed a human-like reliance on 14 underlying conceptual attributes of emotions for emotion inference. Most importantly, by manipulating attribute-specific neurons, we found that the corresponding LLM's emotion inference performance deteriorated, and the performance deterioration was correlated to the effectiveness of representations of the conceptual attributes on the human side. Our findings provide direct evidence for the emergence of emotion knowledge representation in large language models and suggest its casual support for discrete emotion inference.
翻译:人类如何推断离散情绪是心理学领域的一个基本研究问题。虽然关于情感(情感知识)的概念知识被认为对情感推断至关重要,但迄今为止的证据大多是间接的和没有结论的。大型语言模型(LLMs)已经证明支持有效表述各种人类概念知识,因此,本研究报告进一步在LLMs中使用人工神经元来调查人类情感推断机制。随着人造神经因电光而激活,LLLM(ROBERTA)展示了与人类行为相似的27种离散情感的概念结构。此外,基于LLM的概念结构揭示了人类对情感推断的14种基本情感概念属性的依赖。最重要的是,通过操纵特定特性的神经元,我们发现相应的LLM的情感推断性能恶化,而性能恶化与人类方面概念属性表达的有效性相关。我们的调查结果直接证明了在大型语言模型中出现情感知识的体现,并表明其对离散情感推断的随意支持。