arXiv:2512.13441cs.CLq-bio.NC2025-12被引 68
大模型靠统计无法理解语言本质，人类语言是内在递归思维系统。
Large language models are not about natural language
- 大模型依赖海量数据统计外在语符，不涉及内在语言机制
- 人类语言能用极少输入生成复杂结构，并识别不可能语言
- 适合对语言认知本质感兴趣的学者
大型语言模型对语言学无用，因其是概率模型，需大量数据分析外在词语串。相反，人类语言基于内在计算系统，可递归生成层次化思维结构。该语言系统在极少外部输入下即可发展，并能轻易区分真实语言与不可能语言。
原文摘要 · Abstract (English)
Large Language Models are useless for linguistics, as they are probabilistic models that require a vast amount of data to analyse externalized strings of words. In contrast, human language is underpinned by a mind-internal computational system that recursively generates hierarchical thought structures. The language system grows with minimal external input and can readily distinguish between real language and impossible languages.
语言认知大模型局限心智计算
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