Both Matter: Enhancing the Emotional Intelligence of Large Language
  Models without Compromising the General Intelligence
Weixiang Zhao Zhuojun Li Shilong Wang Yang Wang Yulin Hu Yanyan Zhao Chen Wei Bing Qin
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.