Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks, thereby piquing the research community's curiosity for exploring their potential in emotional intelligence. However, several issues in the field of emotional generation tasks remain unresolved, including human preference alignment and emotional generation assessment. In this paper, we propose the Emotional Chain-of-Thought (ECoT), a plug-and-play prompting method that enhances the performance of LLMs on various emotional generation tasks by aligning with human emotional intelligence guidelines. To assess the reliability of ECoT, we propose an automated model-based evaluation method called Emotional Generation Score (EGS). EGS incorporates Goleman's Emotional Intelligence Theory as a consensus of human experts, providing a new perspective on the evaluation of emotional generation tasks. Extensive experimental results demonstrate the effectiveness of ECoT and EGS. Further, we discuss the promise of LLMs in the field of emotional intelligence and present key insights into the LLMs with the ECoT in emotional generation tasks.

翻译：大型语言模型（LLMs）在各种情感识别任务中展现出卓越性能，引发了学术界对其情感智能潜能的探索兴趣。然而，情感生成任务领域仍存在若干未解问题，包括人类偏好对齐与情感生成评估。本文提出情感链式思维（ECoT）——一种即插即用的提示方法，通过遵循人类情感智能准则来提升LLMs在多种情感生成任务中的表现。为评估ECoT的可靠性，我们提出基于自动化模型的情感生成评分（EGS）评估方法。EGS将戈尔曼情感智能理论作为人类专家共识的体现，为情感生成任务评估提供了新视角。大量实验结果表明了ECoT与EGS的有效性。此外，我们探讨了LLMs在情感智能领域的应用前景，并阐述了采用ECoT的LLMs在情感生成任务中的关键洞见。