@inproceedings{huang-2024-ancient,
    title = "{A}ncient {C}hinese Punctuation via In-Context Learning",
    author = "Huang, Jie",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lt4hala-1.33",
    pages = "261--265",
    abstract = "EvaHan2024 focuses on sentence punctuation in ancient Chinese. Xunzi large language base model, which is specifically trained for ancient Chinese processing, is advised in the campaign. In general, we adopted the in-context learning (ICL) paradigm for this task and designed a post-processing scheme to ensure the standardability of final results. When constructing ICL prompts, we did feature extraction by LLM QA and selected demonstrations based on non-parametric metrics. We used Xunzi in two stages and neither did further training, so the model was generic and other fundamental abilities remained unaffected. Moreover, newly acquired training data can be directly utilized after identical feature extraction, showcasing the scalability of our system. As for the result, we achieved an F1-score of 67.7{\%} on a complex test dataset consisting of multiple types of documents and 77.98{\%} on Zuozhuan data.",
}

EvaHan2024 focuses on sentence punctuation in ancient Chinese. Xunzi large language base model, which is specifically trained for ancient Chinese processing, is advised in the campaign. In general, we adopted the in-context learning (ICL) paradigm for this task and designed a post-processing scheme to ensure the standardability of final results. When constructing ICL prompts, we did feature extraction by LLM QA and selected demonstrations based on non-parametric metrics. We used Xunzi in two stages and neither did further training, so the model was generic and other fundamental abilities remained unaffected. Moreover, newly acquired training data can be directly utilized after identical feature extraction, showcasing the scalability of our system. As for the result, we achieved an F1-score of 67.7{\%} on a complex test dataset consisting of multiple types of documents and 77.98{\%} on Zuozhuan data.
Huang, Jie
Ancient Chinese Punctuation via In-Context Learning