  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.
