@inproceedings{jia-etal-2026-intelligent,
title = "Can Intelligent Agents Revolutionize Scale Generation?",
author = "Jia, Chenghao and
Yuan, Zhitao and
Zong, Zhaokang and
Yin, YiFei and
Chen, Zhe and
Lan, Man and
Wu, Shengjun",
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.1674/",
pages = "33495--33522",
ISBN = "979-8-89176-395-1",
abstract = "Measurement scales play a crucial role in quantifying the nuanced dimensions of human cognition and behavior, however, their development typically demands extensive manual labor, and current methodologies lack systematic automation and standardized evaluation. In this paper, we introduce AutoScale, a pioneering multi-agent framework that automates scale development by leveraging collaborative AI agents. Our contributions are threefold: (1) a novel multi-agent LLM-based framework for end-to-end scale generation that replicates expert collaboration and iterative data-driven refinement, (2) the first comprehensive dataset, SCALE-1.2K, comprising 1.2K validated scales across 16 psychological domains, establishing a benchmark for automated scale development, and (3) a multi-dimensional evaluation system, featuring Muti-LLM-as-judge for conceptual and linguistic assessment and simulated large-scale testing for rigorous psychometric verification. Experimental results demonstrate that AutoScale streamlines the scale development process while maintaining rigorous quality standards, significantly reducing manual effort and paving the way for more efficient and objective measurement design in diverse research fields."
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<abstract>Measurement scales play a crucial role in quantifying the nuanced dimensions of human cognition and behavior, however, their development typically demands extensive manual labor, and current methodologies lack systematic automation and standardized evaluation. In this paper, we introduce AutoScale, a pioneering multi-agent framework that automates scale development by leveraging collaborative AI agents. Our contributions are threefold: (1) a novel multi-agent LLM-based framework for end-to-end scale generation that replicates expert collaboration and iterative data-driven refinement, (2) the first comprehensive dataset, SCALE-1.2K, comprising 1.2K validated scales across 16 psychological domains, establishing a benchmark for automated scale development, and (3) a multi-dimensional evaluation system, featuring Muti-LLM-as-judge for conceptual and linguistic assessment and simulated large-scale testing for rigorous psychometric verification. Experimental results demonstrate that AutoScale streamlines the scale development process while maintaining rigorous quality standards, significantly reducing manual effort and paving the way for more efficient and objective measurement design in diverse research fields.</abstract>
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%0 Conference Proceedings
%T Can Intelligent Agents Revolutionize Scale Generation?
%A Jia, Chenghao
%A Yuan, Zhitao
%A Zong, Zhaokang
%A Yin, YiFei
%A Chen, Zhe
%A Lan, Man
%A Wu, Shengjun
%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 jia-etal-2026-intelligent
%X Measurement scales play a crucial role in quantifying the nuanced dimensions of human cognition and behavior, however, their development typically demands extensive manual labor, and current methodologies lack systematic automation and standardized evaluation. In this paper, we introduce AutoScale, a pioneering multi-agent framework that automates scale development by leveraging collaborative AI agents. Our contributions are threefold: (1) a novel multi-agent LLM-based framework for end-to-end scale generation that replicates expert collaboration and iterative data-driven refinement, (2) the first comprehensive dataset, SCALE-1.2K, comprising 1.2K validated scales across 16 psychological domains, establishing a benchmark for automated scale development, and (3) a multi-dimensional evaluation system, featuring Muti-LLM-as-judge for conceptual and linguistic assessment and simulated large-scale testing for rigorous psychometric verification. Experimental results demonstrate that AutoScale streamlines the scale development process while maintaining rigorous quality standards, significantly reducing manual effort and paving the way for more efficient and objective measurement design in diverse research fields.
%U https://aclanthology.org/2026.findings-acl.1674/
%P 33495-33522
Markdown (Informal)
[Can Intelligent Agents Revolutionize Scale Generation?](https://aclanthology.org/2026.findings-acl.1674/) (Jia et al., Findings 2026)
ACL
- Chenghao Jia, Zhitao Yuan, Zhaokang Zong, YiFei Yin, Zhe Chen, Man Lan, and Shengjun Wu. 2026. Can Intelligent Agents Revolutionize Scale Generation?. In Findings of the Association for Computational Linguistics: ACL 2026, pages 33495–33522, San Diego, California, United States. Association for Computational Linguistics.