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In the NLPCC2024 Shared Task 5, our team employed a context-based learning strategy for model fine-tuning. Through meticulously designed prompts and iterative optimization, we successfully enhanced the ability of large pre-trained language models (LLMs) to mine arguments in Chinese argumentative essays. We further incorporated a model voting mechanism to improve prediction accuracy and robustness. Ultimately, our system ranked first in the test set with a composite score of 0.7936.

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Enhancing Chinese Argument Mining with Large Language Model

  • 会议论文

  • 发表日期：

    2025年

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  • Shiquan Wang,
  • Ruiyu Fang,
  • Mengxiang Li,
  • Zhongjiang He,
  • Yongxiang Li,
  • Shuangyong Song

摘要

In the NLPCC2024 Shared Task 5, our team employed a context-based learning strategy for model fine-tuning. Through meticulously designed prompts and iterative optimization, we successfully enhanced the ability of large pre-trained language models (LLMs) to mine arguments in Chinese argumentative essays. We further incorporated a model voting mechanism to improve prediction accuracy and robustness. Ultimately, our system ranked first in the test set with a composite score of 0.7936.