According to Futrell and Mahowald [arXiv:2501.17047], both infants and language models (LMs) find attested languages easier to learn than impossible languages that have unnatural structures. We review the literature and show that LMs often learn attested and many impossible languages equally well. Difficult to learn impossible languages are simply more complex (or random). LMs are missing human inductive biases that support language acquisition.
翻译:根据Futrell和Mahowald的研究[arXiv:2501.17047],婴儿和语言模型(LMs)均认为已证实的语言比具有非自然结构的不可能语言更容易学习。我们通过文献综述发现,语言模型通常能同等程度地掌握已证实语言与多数不可能语言。难以学习的不可能语言仅具有更高复杂度(或随机性)。语言模型缺乏支持人类语言习得所必需的归纳偏置。