@inproceedings{liang-etal-2025-improving-proficiency,
    title = "Improving Proficiency and Grammar Accuracy for {C}hinese Language Learners with Large Language Models",
    author = "Liang, Yuqi  and
      Xu, Wenjing  and
      Xu, Hongzhi",
    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.74/",
    pages = "1216--1232",
    ISBN = "979-8-89176-303-6",
    abstract = "In this study, we evaluate the performance of large language models (LLMs) in detecting and correcting grammatical errors made by Chinese language learners. We find that incorporating various linguistic features{---}such as dependency structures, parts of speech, and pinyin transliteration{---}into the prompts can potentially enhance model performance. Among these features, parts of speech and pinyin prove to be the most effective across all tested models. Additionally, our findings show that the success of error correction also depends on the severity of the errors. When the intended meaning is preserved, LLMs tend to provide accurate revisions following the principle of minimal editing. However, when the meaning is obscured, LLMs are more likely to produce divergent outputs, both in comparison to reference corrections and to the responses of other models."
}