Enhancing the Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought

The Emotional Generation is a subset of emotional intelligence, which aims to output an emotional response based on emotional conditions as input. Emotion generation has a wide range of applications, including emotion chat, emotional visual caption, and emotional rewriting. However, it faces challenges such as a lack of interpretability and poor evaluability. In this paper, we propose the Emotional Chain-of-Thought (ECoT), a plug-and-play prompting method that enhances the performance of Large Language Models (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 EGS. Extensive experimental results demonstrate the effectiveness of ECoT and EGS. Further,we discuss the promise of LLMs in the field of sentiment analysis and present key insights into the LLMs with the ECoT in emotional generation tasks.