GPT的多语言能力:对结构模两可的研究
Myung Hye Yoo1, Joungmin Kim2, Sanghoun Song3
1Department of English Language and Literature, Chungnam National University, Daejeon, South Korea.
PloS one
|July 7, 2025
概括
像GPT这样的大型语言模型在多语言理解方面存在局限性,通常偏爱英语模式,而不是韩语和日语语法含糊性的特定语言细微差别. 为了改善语言多样性参与,需要进一步的研究.
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.
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 展示了先进的语言能力,但它们的多语言处理,特别是关于语法模两可的,仍然未被充分探索.
- 了解LLM如何处理特定语言的语法结构对于开发全球适用的AI至关重要.
研究的目的:
- 调查英语,韩语和日语的各种GPT模型的多语言语法模糊性解决能力.
- 为了确定GPT模型是否表现出以英语为中心的偏见或捕捉特定语言的附件偏好.
主要方法:
- 利用模两可的相对条款来测试GPT模型在不同语言环境中的解释倾向.
- 与英语,韩语和日语的母语使用者偏好的模型解释进行了比较.
主要成果:
- GPT模型 (GPT-3.5-turbo,GPT-4-turbo,GPT 4o) 与英语偏好保持一致,但在韩语上过于泛化,在日语中没有明确的偏好.
- 较小的模型 (o1-mini,o3-mini) 在韩语和日语中反映了英语附件模式.
- 结果表明,在GPT的多语言处理中,偏向于像英语这样的高资源语言.
结论:
- GPT的多语言能力有限,表现出一种将以英语为中心的模式概括的倾向.
- 模型大小和调整策略可能会影响以英语为中心偏差的程度.
- 为了实现对语言多样性的细微处理,LLM需要进一步精细化.
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