@inproceedings{pokharel-agrawal-2025-mtq,
title = "{MTQ}-Eval: Multilingual Text Quality Evaluation for Language Models",
author = "Pokharel, Rhitabrat and
Agrawal, Ameeta",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.79/",
pages = "1289--1304",
ISBN = "979-8-89176-303-6",
abstract = "The use of large language models (LLMs) for evaluating outputs is becoming an increasingly effective and scalable approach. However, it remains uncertain whether this capability extends beyond task-specific evaluations to more general assessments of text quality, particularly in multilingual contexts. In this study, we introduce {--} MTQ-Eval {--} a novel framework for multilingual text quality evaluation. We automatically generate text quality preference data and train open-source base LLMs to align with ratings of high- and low-quality text. Our comprehensive evaluation across 115 languages demonstrates the improved performance of the proposed model. Additionally, we explore whether this enhanced ability to distinguish between high- and low-quality text translates to better performance in downstream tasks."
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<abstract>The use of large language models (LLMs) for evaluating outputs is becoming an increasingly effective and scalable approach. However, it remains uncertain whether this capability extends beyond task-specific evaluations to more general assessments of text quality, particularly in multilingual contexts. In this study, we introduce – MTQ-Eval – a novel framework for multilingual text quality evaluation. We automatically generate text quality preference data and train open-source base LLMs to align with ratings of high- and low-quality text. Our comprehensive evaluation across 115 languages demonstrates the improved performance of the proposed model. Additionally, we explore whether this enhanced ability to distinguish between high- and low-quality text translates to better performance in downstream tasks.</abstract>
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%0 Conference Proceedings
%T MTQ-Eval: Multilingual Text Quality Evaluation for Language Models
%A Pokharel, Rhitabrat
%A Agrawal, Ameeta
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F pokharel-agrawal-2025-mtq
%X The use of large language models (LLMs) for evaluating outputs is becoming an increasingly effective and scalable approach. However, it remains uncertain whether this capability extends beyond task-specific evaluations to more general assessments of text quality, particularly in multilingual contexts. In this study, we introduce – MTQ-Eval – a novel framework for multilingual text quality evaluation. We automatically generate text quality preference data and train open-source base LLMs to align with ratings of high- and low-quality text. Our comprehensive evaluation across 115 languages demonstrates the improved performance of the proposed model. Additionally, we explore whether this enhanced ability to distinguish between high- and low-quality text translates to better performance in downstream tasks.
%U https://aclanthology.org/2025.findings-ijcnlp.79/
%P 1289-1304
Markdown (Informal)
[MTQ-Eval: Multilingual Text Quality Evaluation for Language Models](https://aclanthology.org/2025.findings-ijcnlp.79/) (Pokharel & Agrawal, Findings 2025)
ACL
- Rhitabrat Pokharel and Ameeta Agrawal. 2025. MTQ-Eval: Multilingual Text Quality Evaluation for Language Models. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 1289–1304, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.