@inproceedings{zheng-etal-2025-system,
    title = "System Report for {CCL}25-Eval Task 11: Aesthetic Assessment of {C}hinese Handwritings Based on Vision Language Models",
    author = "Zheng, Chen  and
      Lai, Yuxuan  and
      Lu, Haoyang  and
      Ma, Wentao  and
      Yang, Jitao  and
      Wang, Jian",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    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-2.53/",
    pages = "444--451",
    abstract = "``The handwriting of Chinese characters is a fundamental aspect of learning the Chinese language. Previous automated assessment methods often framed scoring as a regression problem. However, this score-only feedback lacks actionable guidance, which limits its effectiveness in helping learners improve their handwriting skills.In this paper, we leverage vision-language models(VLMs) to analyze the quality of handwritten Chinese characters and generate multi-level feedback. Specifically, we investigate two feedback generation tasks: simple grade feedback (Task 1)and enriched, descriptive feedback (Task 2). We explore both low-rank adaptation (LoRA)-based fine-tuning strategies and in-context learning methods to integrate aesthetic assessment knowl-edge into VLMs. Experimental results show that our approach achieves state-of-the-art performances across multiple evaluation tracks in the CCL 2025 workshop on evaluation of handwrittenChinese character quality.''"
}