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The task of information extraction is confronted with many difficulties in the field of traditional Chinese medicine. On top of that, existing methods, including traditional neural network and emerging large language model-assisted information extraction, show low accuracy and recall rates. In this paper, an instruction fine-tuning paradigm is proposed for traditional Chinese medicine text information extraction, which has achieved ideal results on data sets.

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Enhancing Traditional Chinese Medicine Information Extraction Using Instruction-Tuned Large Models

  • 会议论文

  • 发表日期：

    2025年

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  • Jingyao Chen,
  • Shuqi Xia,
  • Jinghua Li,
  • Tong Yu

摘要

The task of information extraction is confronted with many difficulties in the field of traditional Chinese medicine. On top of that, existing methods, including traditional neural network and emerging large language model-assisted information extraction, show low accuracy and recall rates. In this paper, an instruction fine-tuning paradigm is proposed for traditional Chinese medicine text information extraction, which has achieved ideal results on data sets.