在LLM的极限上,surprisal作为N400和P600的功能解释
Benedict Krieger1, Harm Brouwer2, Christoph Aurnhammer1
1Department of Language Science and Technology, Saarland University, Germany.
Brain research
|August 9, 2025
概括
大型语言模型 (LLM) 为语言理解提供了令人惊的估计,但它们不能始终预测像N400和P600这样的神经反应. LLM surprisal受词汇关联的影响,限制了其模拟这些大脑信号的能力.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 神经科学是一个神经科学.
背景情况:
- 语言理解依赖于对即将到来的单词的期望,处理方便与可预测性相关.
- 惊喜理论根据一个词的上下文概率量化认知努力.
- 大型语言模型 (LLM) 可以计算上下文惊喜估计,促使它们作为语言理解模型的使用.
研究的目的:
- 评估LLM衍生的惊喜和语言理解的两个神经相关之间的关系:N400和P600事件相关潜力 (ERP).
- 评估LLM惊喜是否可以解释ERP在德国语言处理中观察到的差异.
- 与N400相比,研究P600作为一种更直接的惊喜神经指数的潜力.
主要方法:
- 利用基于回归的ERP (rERP) 分析,对来自德国三项因数学研究的数据进行分析.
- 将LLM的意外估计与N400和P600组件进行了比较.
- 检查了语义关联与上下文预期对ERP和LLM意外的影响.
主要成果:
- 在LLM中,surprisal不一致地解释了N400或P600组件.
- 发现LLM surprisal被简单的单词关联污染了,特别是在较小的模型中.
- LLM的惊喜部分解释了协会驱动的N400效应,但不是它们的全部衰减,以及对P600的妥协建模.
结论:
- 目前的LLM,由于它们对关联的敏感性,在完全模拟语言理解神经动态的能力方面受到限制,如N400和P600所反映的那样.
- 虽然P600 ERP组件对预期敏感,但由于混的关联效应,LLM surprisal不能充分模拟它.
- 需要进一步开发LLM来准确地捕捉语言期望,惊喜和理解过程中的神经处理之间的细微关系.
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