Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence (2024)

Abstract: 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 EiBench, a large-scale collection of EI-related tasks in the text-to-text format 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 Modular Emotional Intelligence enhancement method (**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.

## introduction the question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions. ## eibench we first introduce eibench, a large-scale collection of ei-related tasks with instructions that describe them in plain language. figure 2 shows the overview of the benchmark by category and task. ## methodology instead of merely focusing on enhancing specific capabilities of llms, we present a novel approach moei, a model-agnostic ei-enhancing method that is compatible with any transformer-based llm, which could not only comprehensively boost the ei of llms but also safeguards their gi from being compromised. as shown in figure 3 , moei consists of two collaborative techniques, namely modular parameter expansion and intra-inter modulation. the subsequent section will offer a detailed introduction to both of them. ## conclusion and future work in this paper, we take the first step to study the challenging and demanding research topic of enhancing the ei of current llm-empowered assistants while maintaining their gi. we introduce eibench, a comprehensive collection comprising large-scale tasks that encompass all facets of ei: emotion perception, cognition, and expression. our innovative approach, moei, ingeniously integrates two collaborative techniques, modular parameter expansion and intra-inter modulation, to introduce additional modular parameter-efficient lora blocks for the accomodation of newly acquired ei-related competencies, and automatically navigate ei-and gi-related inputs to be properly processed by the corresponding parameters. extensive experimental results demonstrate the applicability of moei on llm backbones of varying scales and architectures, highlighting its versatility. for future work, exploring the quality and quantity of ei-and gi-related data in the process of ei enhancement is an intriguing direction to further enhance the effectiveness and efficiency of ei enhancement and gi maintenance.