机器翻译是大型语言模型的:依赖性三胞胎,文本分类和SHAP分析
Shukang Zhang1, Chaoyong Zhao1
1School of Foreign Languages, East China Normal University, Shanghai, China.
PloS one
|January 9, 2026
概括
本研究将人类翻译与大型语言模型 (LLM) 输出区分开来,使用依赖特性和机器学习. 支持矢量机 (SVM) 模型实现了93%的准确性,为LLM翻译提供了洞察力.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 区分人类翻译和机器翻译对于质量评估至关重要.
- 大型语言模型 (LLM) 生成的翻译可能很难与人类工作区分开来.
- 了解LLM生成文本 (翻译) 的语言特征是一个持续的挑战.
研究的目的:
- 开发和评估区分人类翻译与LLM生成的翻译的方法.
- 确定区分人类和机器翻译的关键语言特征.
- 评估各种机器学习分类器在此任务中的表现.
主要方法:
- 利用了从翻译数据集中提取的依赖三重组功能.
- 评估了16种不同的机器学习分类器.
- 采用十倍交叉验证来进行可靠的绩效评估.
- 应用SHAP (夏普利添加式解释) 分析来解释模型决策.
主要成果:
- 支持矢量机 (SVM) 模型实现了最高的平均F1得分93%.
- 所有评估的分类器都表现出一致的能力,能够区分人类和机器翻译.
- 在SHAP分析中,确定了特定的依赖特征,这些特征对于区分翻译来源至关重要.
结论:
- 依赖三重体的特征是有效的区分人类从LLM翻译.
- 机器学习分类器,特别是SVM,在这个分类任务中表现出很高的表现.
- 该研究增强了对LLM翻译的理解,并为翻译质量评估和模型开发的改进提供了信息.
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