Large Language Models are not Models of Natural Language: they are Corpus Models

Abstract: Natural Language Processing (NLP) has become one of the leading application areas in the current Artificial Intelligence boom. Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance in almost all downstream language tasks. Interestingly, when the language models are trained with data that includes software code, they demonstrate remarkable abilities in generating functioning computer code from natural language specifications. We argue that this creates a conundrum for the claim that eliminative neural models are a radical restructuring in our understanding of cognition in that they eliminate the need for symbolic abstractions like generative phrase structure grammars. Because the syntax of programming languages is by design determined by phrase structure grammars, neural models that produce syntactic code are apparently uninformative about the theoretical foundations of programming languages. The demonstration that neural models perform well on tasks that involve clearly symbolic systems, proves that they cannot be used as an argument that language and other cognitive systems are not symbolic. Finally, we argue as a corollary that the term language model is misleading and propose the adoption of the working term corpus model instead, which better reflects the genesis and contents of the model.