Scholarlink

This paper aims to address the challenge of correcting semantic, syntactic, and knowledge-related errors in Chinese government texts. The existing methods often overlook content-level inaccuracies and knowledge inconsistencies. To address the above issue, we propose a knowledge-aware rewriting method with large language models (LLMs) for Chinese government text correction (in short KARTC). The proposed KARTC comprehensively integrates structured knowledge bases (KBs) with LLMs with a three-stage hierarchical task-chain framework. To demonstrate the effectiveness of the proposed KARTC, we conduct the experiments on NLPCC 2025 shared task 5. The results show that KARTC achieves the 79.64% in term of accuracy, and ranks the 2nd place on NLPCC 2025 shared task 5.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

KARTC: Knowledge-Aware Rewriting with Large Language Models for Chinese Government Text Correction

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Lei Hu,
  • Wenting Zhang,
  • Lihua Tan,
  • Zhiwen Xie,
  • Guangyou Zhou

摘要

This paper aims to address the challenge of correcting semantic, syntactic, and knowledge-related errors in Chinese government texts. The existing methods often overlook content-level inaccuracies and knowledge inconsistencies. To address the above issue, we propose a knowledge-aware rewriting method with large language models (LLMs) for Chinese government text correction (in short KARTC). The proposed KARTC comprehensively integrates structured knowledge bases (KBs) with LLMs with a three-stage hierarchical task-chain framework. To demonstrate the effectiveness of the proposed KARTC, we conduct the experiments on NLPCC 2025 shared task 5. The results show that KARTC achieves the 79.64% in term of accuracy, and ranks the 2nd place on NLPCC 2025 shared task 5.