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深度神经网络和人类都受益于组成的语言结构结构
Lukas Galke1,2, Yoav Ram3,4, Limor Raviv5,6
1Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark. galke@imada.sdu.dk.
Nature communications
|December 31, 2024
概括
深度神经网络 (DNN) 更好地学习结构化语言,类似于人类. 更多的组成语言导致了DNN中的系统概括,改善了它们作为人类语言获取模型的使用.
科学领域:
- 计算语言学 计算语言学
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 深度神经网络 (DNN) 在自然语言处理方面出色.
- 人类语言学习倾向于构成和透明的结构.
- 在结构化语言输入方面,DNN的优势仍然未被充分探索.
研究的目的:
- 为了比较人类和DNN学习的语言,具有不同的组成结构.
- 调查DNN是否对结构化语言具有可学习性的优势.
- 评估DNN作为人类语言学习模型的潜力.
主要方法:
- 评估大型语言模型和循环神经网络.
- 测试记忆和概括能力.
- 在具有不同组成度的语言中比较DNN性能.
主要成果:
- DNN显示出一种可学习性的优势,用于更复杂的语言.
- 结构化的语言输入导致在DNN中更有系统的概括.
- DNN的学习模式与人类学习者更紧密地结合了组成语言的学习模式.
结论:
- 像人类一样,DNN受益于语言结构.
- 复合性增强了深度学习模型中的系统概括性.
- 研究结果支持使用DNNs作为人类语言学习的模型.
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