@inproceedings{haihong-etal-2024-assessing,
    title = "Assessing Essay Fluency with Large Language Models",
    author = "Haihong, Wu  and
      Chang, Ao  and
      Shiwen, Ni",
    editor = "Lin, Hongfei  and
      Tan, Hongye  and
      Li, Bin",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.ccl-3.29/",
    pages = "262--268",
    language = "eng",
    abstract = "{\textquotedblleft}With the development of education and the widespread use of the internet, the scale of essay evaluation has increased, making the cost and efficiency of manual grading a significant challenge. To address this, The Twenty-third China National Conference on Computational Linguistics (CCL2024) established evaluation contest for essay fluency. This competition has three tracks corresponding to three sub-tasks. This paper conducts a detailed analysis of different tasks,employing the BERT model as well as the latest popular large language models Qwen to address these sub-tasks. As a result, our overall scores for the three tasks reached 37.26, 42.48, and 47.64.{\textquotedblright}"
}