@inproceedings{chen-2024-sentence,
    title = "Sentence Segmentation and Sentence Punctuation Based on {X}unzi{ALLM}",
    author = "Chen, Zihong",
    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-1.30",
    pages = "246--250",
    abstract = "In ancient Chinese books, punctuation marks are typically absent in engraved texts. Sentence segmentation and punctuation heavily rely on the meticulous efforts of experts and scholars. Therefore, the work of automatic punctuation and sentence segmentation plays a very important role in promoting ancient books, as well as the inheritance of Chinese culture. In this paper, we present a method for fine-tuning downstream tasks for large language model using the LoRA approach, leveraging the EvaHan2024 dataset. This method ensures robust output and high accuracy while inheriting the knowledge from the large pre-trained language model Xunzi.",
}

In ancient Chinese books, punctuation marks are typically absent in engraved texts. Sentence segmentation and punctuation heavily rely on the meticulous efforts of experts and scholars. Therefore, the work of automatic punctuation and sentence segmentation plays a very important role in promoting ancient books, as well as the inheritance of Chinese culture. In this paper, we present a method for fine-tuning downstream tasks for large language model using the LoRA approach, leveraging the EvaHan2024 dataset. This method ensures robust output and high accuracy while inheriting the knowledge from the large pre-trained language model Xunzi.
Chen, Zihong
Sentence Segmentation and Sentence Punctuation Based on XunziALLM