Towards a neural architecture of language: Deep learning versus logistics of access in neural architectures for compositional processing. (arXiv:2210.10543v1 [cs.CL])

Recently, a number of articles have argued that deep learning models such as
GPT could also capture key aspects of language processing in the human mind and
brain. However, I will argue that these models are not suitable as neural
models of human language. Firstly, because they fail on fundamental boundary
conditions, such as the amount of learning they require. This would in fact
imply that the mechanisms of GPT and brain language processing are
fundamentally different. Secondly, because they …

architecture architectures arxiv deep learning language logistics neural architectures processing