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<p>Large language models (LLMs) hold immense potential for the intelligent processing of classical texts. They offer new approaches for digital research on classical literature resources, cross-linguistic understanding, text knowledge mining, and the promotion and preservation of cultural heritage. To explore the performance of named entity recognition (NER) tasks supported by LLMs, this study first fine-tuned four LLMs—Xunzi-Baichuan, Baichuan2-7B-Base, Xunzi-GLM, and ChatGLM3-6B—using supervised fine-tuning methods based on open-source models. Zero-shot, one-shot, and few-shot prompting methods were then employed to validate the performance of these models in the NER tasks. Finally, the applicability of fine-tuning LLMs in specific domains for NER tasks was examined using BLEU-4, ROUGE-1, ROUGE-2, ROUGE-L, precision, recall, and F1 scores as evaluation metrics for model performance and applicability. The experimental results indicated that fine-tuned LLMs achieved high scores across multiple metrics, demonstrating strong performance in text generation. In entity extraction, the Xunzi-Baichuan model performed optimally across several metrics and also exhibited generalization capabilities. In addition, we have open-sourced our models for community research. <a href="https://github.com/Xunzi-LLM-of-Chinese-classics/XunziALLM">https://github.com/Xunzi-LLM-of-Chinese-classics/XunziALLM</a>.</p>

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Automatic recognition of cross-language classic entities based on large language models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月3日

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  • Qiankun Xu,
  • Yutong Liu,
  • Dongbo Wang,
  • Shuiqing Huang

摘要

Large language models (LLMs) hold immense potential for the intelligent processing of classical texts. They offer new approaches for digital research on classical literature resources, cross-linguistic understanding, text knowledge mining, and the promotion and preservation of cultural heritage. To explore the performance of named entity recognition (NER) tasks supported by LLMs, this study first fine-tuned four LLMs—Xunzi-Baichuan, Baichuan2-7B-Base, Xunzi-GLM, and ChatGLM3-6B—using supervised fine-tuning methods based on open-source models. Zero-shot, one-shot, and few-shot prompting methods were then employed to validate the performance of these models in the NER tasks. Finally, the applicability of fine-tuning LLMs in specific domains for NER tasks was examined using BLEU-4, ROUGE-1, ROUGE-2, ROUGE-L, precision, recall, and F1 scores as evaluation metrics for model performance and applicability. The experimental results indicated that fine-tuned LLMs achieved high scores across multiple metrics, demonstrating strong performance in text generation. In entity extraction, the Xunzi-Baichuan model performed optimally across several metrics and also exhibited generalization capabilities. In addition, we have open-sourced our models for community research. https://github.com/Xunzi-LLM-of-Chinese-classics/XunziALLM.