语言模型与人类大脑之间的分歧
Yuchen Zhou1, Emmy Liu1, Graham Neubig1
1Carnegie Mellon University.
ArXiv
|January 29, 2025
概括
大型语言模型 (LLM) 显示了与人类语言处理的差异,特别是在社会/情绪智能和物理常识方面. 在这些领域微调LLM可以改善它们与人类大脑反应的对齐.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 人工智能的人工智能
背景情况:
- 最近的研究表明,语言模型 (LM) 和人类大脑在语言处理中共享计算原理.
- 然而,在LM和人类如何表示和利用语言方面存在显著差异.
研究的目的:
- 系统地探索人类和机器语言处理之间的差异.
- 确定与人类认知相比,LM缺少的特定领域.
主要方法:
- 使用磁脑电图 (MEG) 检查了LM表示和人类大脑反应之间的差异.
- 分析了两个数据集,对受试者阅读和倾听叙事故事.
- 采用基于大型语言模型 (LLM) 的数据驱动方法.
主要成果:
- 确定了社会/情绪智力和身体常识作为当前LMS很少捕捉到的领域.
- 通过人类行为实验验证实了这些发现.
- 观察到,对这些特定领域的LM进行微调可以提高它们与人类大脑活动的对齐.
结论:
- 在LM表现中的差距很可能是由于社会/情感和身体知识的代表性不足.
- 微调的LM可以在这些关键领域弥合机器和人类语言处理之间的差距.
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