Year: 2026
Type: conference-paper
Abstract: Emotional Quotient (EQ) has emerged as a competency for seamless human-AI integration.However, since traditional EQ scales focus on self-healing, directly migrating them to Large Language Models (LLMs) often leads to ignorance of healing others.While EQ metrics specifically designed for LLMs have been proposed, they remain mired in two dilemmas: dimensional deficiency and fragmented testing.Hence, this paper establishes a Quad-in-One architecture for a closed-loop EQ evaluation.First, we propose the PACE Taxonomy to define four dimensions of LLM EQ.Upon this, the Causal-PACE framework is developed to eliminate causal confounding bias triggered by the interactions among EQ dimensions, ensuring a rigorous quantification of composite EQ scores.To operationalize this framework, we implement the PACE-AB, a mutil-agent EQ evaluation board system.Finally, we curate the PACE-2700 dataset, featuring 2,700 highquality instructions, to serve as a comprehensive benchmark for large-scale validation.Experimental results demonstrate that the EQ values derived via the Causal-PACE achieve a high alignment of 89.31% with human preferences, while the automated PACE-AB system maintains a robust consistency of 83.6%.Our data is publicly available at https://anonymous. 4open.science/r/PACE-2700-8E52.
Authors: Lei Lyu, Shengling Wang, Ke Chao, Yichao Wei
Institution: Beijing Normal University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166830557