[目的/意义]大语言模型的指令遵循、思维链及推理能力为古籍文本的自动词性标注任务提供了新的契机，有利于促进古籍智能信息处理研究工作的开展。[方法/过程]以人工校验的《二十四史》古现词性标注语料为基础，运用LoRA方法对主流中文大语言模型进行高效监督微调，并测试SFT模型的零样本（Zero-shot）和单样本（One-shot）学习能力，以比较其在古现文本分词与词性标注上的性能。[结果/结论]经过微调后的Xunzi-Baichuan模型整体表现最优，古文本的分词和词性标注F1得分分别为92.293%和85.75%，现代汉语的分词和词性标注F1得分为91.993%和86.344%。

[Purpose/significance] The instruction following, chain of thought and reasoning ability of large language models provide new opportunities for the automatic part-of-speech tagging task of ancient texts, which is conducive to the promotion of the research work on intelligent information processing of ancient texts. [Method/process] In this study, based on a manually calibrated part-of-speech tagging corpus of the Twenty-four Histories, we apply the LoRA method to efficiently supervise the fine-tuning of mainstream Chinese LLMs, and test the zero-shot and one-shot learn⁃ing ability of the SFT model in order to compare its performance in word segmentation and part-of-speech tagging of ancient texts. [Result/conclusion] The test found that the fine-tuned Xunzi-Baichuan model has the best overall per⁃formance, with F1 scores of 92.293% and 85.75% for word segmentation and part-of-speech tagging of ancient texts,respectively, while the F1 scores for word segmentation and part-of-speech tagging of modern Chinese are 91.993%
and 86.344%.