@inproceedings{cai-zhu-2025-mqm,
    title = "{MQM}-{MSC}: Enhancing Translation Quality Estimation Interpretability with Mask-Driven Self-Correction in Large Language Models",
    author = "Cai, Guanghui  and
      Zhu, Junguo",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-1.67/",
    pages = "880--889",
    abstract = "``Large Language Models (LLMs) have demonstrated significant potential in interpretable translation quality estimation by providing both holistic ratings and fine-grained feedback. However,state-of-the-art methods, such as GEMBA-MQM, still suffer from an excessive number of false positives in error prediction, leading to misalignment with human annotations and reducing interpretability. To address this issue, we propose MQM-MSC, a novel training-free framework that employs a mask-driven self-correction (MSC) mechanism. The core of MSC is to use masks to highlight error spans in the initial prediction, enabling the model to re-evaluate these masked portions and verify their correctness. This approach mirrors human cognitive processes: when individuals express inconsistent judgments about the same issue at different times, it often indicates that their initial assessment was flawed. Similarly, MSC exploits contradictions between two evaluations to identify and filter false positives, thereby improving the accuracy and reliability of error annotations. Experimental results show that MQM-MSC effectively reduces false positives across four LLMs and three language pairs, consistently improving the reliability and quality of error annotations in the GEMBA-MQM approach''"
}