一种基于大型语言模型的多代理经典中文翻译方法
Weifeng Lv1, Qiong Cao2, Xiaoyang Liu1
1College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China.
Scientific reports
|November 17, 2025
概括
本研究引入了一种基于大型语言模型 (LLM) 的经典中文翻译框架,显著提高了对现有方法的准确性和文化忠实性.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
- 数字人文学科 数字人文学科
背景情况:
- 经典中文翻译面临着手动方法和当前机器翻译 (MT) 方法的挑战.
- 现有的MT和大型语言模型 (LLM) 在古典中文的语义细微差别和文化特点上扎.
研究的目的:
- 开发一个LLM驱动的多代理框架,以提高古典汉语翻译质量.
- 解决语义准确性,文化忠实性和自动翻译的一致性方面的局限性.
主要方法:
- 一个多代理框架,将翻译分解为文字解释,段落生成和审查.
- 整合一个关键词解释数据库,检索增强生成和代反循环.
- 使用LLMs进行细微的解释和生成,得到专业数据库和审查代理的支持.
主要成果:
- 与单一模型基线相比,BLEURT,BLEU-1和METEOR得分得到了18.8-25.7%的改善.
- 显示得分差异减少了12.7%,这表明翻译稳定性得到了提高.
- 人类的评估证实了卓越的流利性,充分性和文化忠诚度,特别是与较弱的基线相比.
结论:
- 拟议的LLM驱动的多代理框架显著推进了古典汉语翻译.
- 该框架有效地处理多语法,文化暗示和语义连贯性.
- 这种方法为翻译其他历史悠久或低资源语言提供了可转移的模型,从而保护文化遗产.
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