@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://preview.aclanthology.org/ingest-ijcnlp-aacl/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."
}