人类和神经语言模型中的连续循环运动:测试wh-filler-gap依赖性
1Department of English Language and Literature, Dongguk University, Seoul, Republic of Korea.
Frontiers in psychology
|January 26, 2026
概括
像GPT-2这样的大型语言模型不像人类那样处理语法. 这些自回归模型无法理解隐藏的短语结构,从而挑战了它们的认知可信性.
科学领域:
- 计算语言学 计算语言学
- 认知科学 认知科学
- 心理语言学 心理语言学
背景情况:
- 当代的自回归语言模型 (例如,GPT-2,GPT-Neo,OPT) 越来越多地用于模拟人类语言处理.
- 然而,它们对抽象语法结构捕捉人类类似敏感性的能力仍未得到充分研究.
研究的目的:
- 调查自回归语言模型是否复制人类对隐藏的中间短语结构 (CP与NP) 的敏感性.
- 检查wh-filler-gap依赖性和反向锁定的模型处理.
- 评估这些模型作为人类语法能力的表现的认知合理性.
主要方法:
- 两个实验测量了使用surpris.al的处理难度.
- 评估了GPT风格模型对隐藏的短语结构 (CP与NP) 在wh-dependencies和向后 sluicing中的敏感性.
- 将模型处理模式与已确定的人类心理语言数据进行比较.
主要成果:
- 观察到与人类处理模式的显著差异.
- 模型未能为正规依赖关系和消除依赖关系复制人类处理便利性.
- 一个模型表现出相反的效果,表明依赖表面线索而不是等级结构.
结论:
- 经过测试的GPT风格模型不足以获得隐藏的语法结构的知识.
- 这些发现为"刺激的贫困" (PoS) 论点提供了实证支持.
- 突出了当前NLM用于建模人类语法能力的认知可信性的重大差距.
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