@inproceedings{jin-etal-2025-multi,
title = "A Multi-persona Framework for Argument Quality Assessment",
author = "Jin, Bojun and
Bao, Jianzhu and
Hou, Yufang and
Sun, Yang and
Zhang, Yice and
Wang, Huajie and
Liang, Bin and
Xu, Ruifeng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.593/",
doi = "10.18653/v1/2025.acl-long.593",
pages = "12148--12170",
ISBN = "979-8-89176-251-0",
abstract = "Argument quality assessment faces inherent challenges due to its subjective nature, where different evaluators may assign varying quality scores for an argument based on personal perspectives. Although existing datasets collect opinions from multiple annotators to model subjectivity, most existing computational methods fail to consider multi-perspective evaluation. To address this issue, we propose MPAQ, a multi-persona framework for argument quality assessment that simulates diverse evaluator perspectives through large language models. It first dynamically generates targeted personas tailored to an input argument, then simulates each persona{'}s reasoning process to evaluate the argument quality from multiple perspectives. To effectively generate fine-grained quality scores, we develop a coarse-to-fine scoring strategy that first generates a coarse-grained integer score and then refines it into a fine-grained decimal score. Experiments on IBM-Rank-30k and IBM-ArgQ-5.3kArgs datasets demonstrate that MPAQ consistently outperforms strong baselines while providing comprehensive multi-perspective rationales."
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<abstract>Argument quality assessment faces inherent challenges due to its subjective nature, where different evaluators may assign varying quality scores for an argument based on personal perspectives. Although existing datasets collect opinions from multiple annotators to model subjectivity, most existing computational methods fail to consider multi-perspective evaluation. To address this issue, we propose MPAQ, a multi-persona framework for argument quality assessment that simulates diverse evaluator perspectives through large language models. It first dynamically generates targeted personas tailored to an input argument, then simulates each persona’s reasoning process to evaluate the argument quality from multiple perspectives. To effectively generate fine-grained quality scores, we develop a coarse-to-fine scoring strategy that first generates a coarse-grained integer score and then refines it into a fine-grained decimal score. Experiments on IBM-Rank-30k and IBM-ArgQ-5.3kArgs datasets demonstrate that MPAQ consistently outperforms strong baselines while providing comprehensive multi-perspective rationales.</abstract>
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%0 Conference Proceedings
%T A Multi-persona Framework for Argument Quality Assessment
%A Jin, Bojun
%A Bao, Jianzhu
%A Hou, Yufang
%A Sun, Yang
%A Zhang, Yice
%A Wang, Huajie
%A Liang, Bin
%A Xu, Ruifeng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F jin-etal-2025-multi
%X Argument quality assessment faces inherent challenges due to its subjective nature, where different evaluators may assign varying quality scores for an argument based on personal perspectives. Although existing datasets collect opinions from multiple annotators to model subjectivity, most existing computational methods fail to consider multi-perspective evaluation. To address this issue, we propose MPAQ, a multi-persona framework for argument quality assessment that simulates diverse evaluator perspectives through large language models. It first dynamically generates targeted personas tailored to an input argument, then simulates each persona’s reasoning process to evaluate the argument quality from multiple perspectives. To effectively generate fine-grained quality scores, we develop a coarse-to-fine scoring strategy that first generates a coarse-grained integer score and then refines it into a fine-grained decimal score. Experiments on IBM-Rank-30k and IBM-ArgQ-5.3kArgs datasets demonstrate that MPAQ consistently outperforms strong baselines while providing comprehensive multi-perspective rationales.
%R 10.18653/v1/2025.acl-long.593
%U https://aclanthology.org/2025.acl-long.593/
%U https://doi.org/10.18653/v1/2025.acl-long.593
%P 12148-12170
Markdown (Informal)
[A Multi-persona Framework for Argument Quality Assessment](https://aclanthology.org/2025.acl-long.593/) (Jin et al., ACL 2025)
ACL
- Bojun Jin, Jianzhu Bao, Yufang Hou, Yang Sun, Yice Zhang, Huajie Wang, Bin Liang, and Ruifeng Xu. 2025. A Multi-persona Framework for Argument Quality Assessment. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12148–12170, Vienna, Austria. Association for Computational Linguistics.