Emotional Intelligence (EI), consisting of emotion perception, emotion cognition and emotion expression, plays the critical roles in improving user interaction experience for the current large language model (LLM) based conversational general AI assistants. Previous works mainly focus on raising the emotion perception ability of them via naive fine-tuning on EI-related classification or regression tasks. However, this leads to the incomplete enhancement of EI and catastrophic forgetting of the general intelligence (GI). To this end, we first introduce \textsc{EiBench}, a large-scale collection of EI-related tasks in the text-to-text formation with task instructions that covers all three aspects of EI, which lays a solid foundation for the comprehensive EI enhancement of LLMs. Then a novel \underline{\textbf{Mo}}dular \underline{\textbf{E}}motional \underline{\textbf{I}}ntelligence enhancement method (\textbf{MoEI}), consisting of Modular Parameter Expansion and intra-inter modulation, is proposed to comprehensively enhance the EI of LLMs without compromise their GI. Extensive experiments on two representative LLM-based assistants, Flan-T5 and LLaMA-2-Chat, demonstrate the effectiveness of MoEI to improving EI while maintain GI.

翻译：情绪智能（EI）包含情绪感知、情绪认知和情绪表达三个方面，对于提升当前基于大语言模型（LLM）的通用对话式AI助手的用户交互体验至关重要。先前的工作主要通过针对EI相关分类或回归任务的简单微调来提升其情绪感知能力。然而，这会导致EI提升不完整以及通用智能（GI）的灾难性遗忘。为此，我们首先引入\textsc{EiBench}——一个涵盖EI所有三个方面的文本到文本格式的大型EI任务指令集合，为全面增强LLM的EI奠定坚实基础。接着提出一种新型的\underline{\textbf{Mo}}模块化\underline{\textbf{E}}情绪\underline{\textbf{I}}智能增强方法（\textbf{MoEI}），该方法由模块参数扩展和内外调制组成，可在不损害GI的前提下全面提升LLM的EI。在两个代表性LLM助手Flan-T5和LLaMA-2-Chat上进行的大量实验证明了MoEI在提升EI的同时保持GI的有效性。