@inproceedings{lyu-etal-2026-pace,
title = "At Your Own {PACE}: A Causal Framework for Evaluating {EQ} in {LLM}s",
author = "Lyu, Lei and
Wang, Shengling and
Chao, Ke and
Wei, Yichao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.623/",
doi = "10.18653/v1/2026.findings-acl.623",
pages = "12811--12826",
ISBN = "979-8-89176-395-1",
abstract = "Emotional Quotient (EQ) has emerged as a competency for seamless human-AI integration. However, since traditional EQ scales focus on \textit{self-healing}, directly migrating them to Large Language Models (LLMs) often leads to ignorance of \textit{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 \textbf{PACE Taxonomy} to define four dimensions of LLM EQ. Upon this, the \textbf{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 \textbf{PACE-AB}, a mutil-agent EQevaluation board system. Finally, we curate the \textbf{PACE-2700} dataset, featuring 2,700 high-quality 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 \url{https://anonymous.4open.science/r/PACE-2700-8E52}."
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<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 EQevaluation board system. Finally, we curate the PACE-2700 dataset, featuring 2,700 high-quality 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.</abstract>
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%0 Conference Proceedings
%T At Your Own PACE: A Causal Framework for Evaluating EQ in LLMs
%A Lyu, Lei
%A Wang, Shengling
%A Chao, Ke
%A Wei, Yichao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lyu-etal-2026-pace
%X 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 EQevaluation board system. Finally, we curate the PACE-2700 dataset, featuring 2,700 high-quality 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.
%R 10.18653/v1/2026.findings-acl.623
%U https://aclanthology.org/2026.findings-acl.623/
%U https://doi.org/10.18653/v1/2026.findings-acl.623
%P 12811-12826
Markdown (Informal)
[At Your Own PACE: A Causal Framework for Evaluating EQ in LLMs](https://aclanthology.org/2026.findings-acl.623/) (Lyu et al., Findings 2026)
ACL