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大型语言模型是否像人类一样学习:形态学习中的交联和间隔实践

Ying Xiong¹, Shiyu Wu¹

¹Shanghai Jiao Tong University, China.

Acta psychologica

|September 6, 2025

概括

人类和大型语言模型 (LLM) 的学习方式不同. 但它们没有复制人类的学习策略,比如交织和间隔效应.

科学领域:

认知科学
人工智能
计算语言学

背景情况:

了解人类学习机制对于开发先进的人工智能至关重要.
大型语言模型 (LLM) 在语言学习方面表现有前途,但其学习过程尚未完全被理解.
研究LLM如何学习人工语言可以揭示与人类认知相比的根本差异.

研究的目的:

为了比较人类与LLM之间在人工语言中获得的形态模式.
检查输入结构 (阻断与交错,并置与间隔) 在两种系统中如何影响学习.
发现学习机制和认知策略的差异.

主要方法:

使用一种人工语言学习模式.
参与者包括人类和三个LLM (GPT4mini,DeepSeek_V3,Llama3.1).
用不同的输入序列和表达方式进行了动词分类和曲任务.

主要成果:

它们的表现始终优于人类,
人类的学习受到交错和间隔效应的影响, 这与认知理论一致.
对输入结构的响应依赖于模型,与人类的学习模式有所不同.
与大多数LLM不同的是,人类对学习偏好表现出了超认知错觉.

结论:

人类的学习机制,如交错和间隔,并不能直接转化为LLM.
虽然LLM对输入结构敏感,但与人类的认知方式不同.
不同的LLM架构对输入序列和呈现具有不同程度的敏感性.

关键词:

交叉效应大型语言模型 (LLM)超级认知形态学习间隔效应

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