社区的大小而不是语法复杂性更好地预测了大型语言模型在新的Wug测试中的准确性
Nikoleta Pantelidou1, Evelina Leivada1,2, Raquel Montero1
1Departament de Filologia Catalana, Centre de Lingüística Teòrica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain.
PloS one
|March 11, 2026
概括
大型语言模型在形态概括中显示出类似人类的准确性,但它们的性能是由数据可用性驱动的,而不是语言复杂性. 这表明它与人类语言能力有表面的相似之处.
科学领域:
- 计算语言学 计算语言学
- 人工智能的人工智能
- 心理语言学 心理语言学
背景情况:
- 大型语言模型 (LLM) 表现出复杂的语言行为,引发了关于它们真正能力的争论.
- 评估LLM对新词的形态概括对于理解他们的语言能力至关重要.
- 之前的研究对影响LLM绩效的因素提出了不同的结论.
研究的目的:
- 通过使用新词来研究用新词对形态概括的LLM绩效.
- 为了将LLM准确度与不同语言的人类发言人进行比较.
- 确定语言复杂性或社区规模/数据可用性是否主要影响LLM准确性.
主要方法:
- 一个多语言的Wug测试适应被用来评估六个LLMs.
- 在四种语言中测试模型:加泰罗尼亚语,英语,希腊语和西班牙语.
- 士学位的表现与人类演讲者的准确性进行了基准测试.
主要成果:
- 在将形态过程概括为未见的单词方面,LLM表现出类似人类的准确性.
- 模型准确性与语言社区规模和数据可用性比结构复杂性更有联系.
- 与资源较少的语言 (例如,加泰罗尼亚语,希腊语) 相比,在拥有更大的说者群体和更大的数字资源 (例如,西班牙语,英语) 的语言中观察到更高的准确性.
结论:
- LLM语言表现似乎主要是由培训数据的数量和丰富性驱动的.
- 对语法复杂性的敏感性似乎不是LLM准确性的主要驱动力.
- 法学士可能表现出与人类语言能力的表面相似之处,而不是真正的理解.
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