Why transformers are obviously good models of language

Nobody knows how language works, but many theories abound. Transformers are a class of neural networks that process language automatically with more success than alternatives, both those based on neural computations and those that rely on other (e.g. more symbolic) mechanisms. Here, I highlight direct connections between the transformer architecture and certain theoretical perspectives on language. The empirical success of transformers relative to alternative models provides circumstantial evidence that the linguistic approaches that transformers embody should be, at least, evaluated with greater scrutiny by the linguistics community and, at best, considered to be the currently best available theories. Among the many unknown things about language is why we give specific names to particular objects or categories in the world around us. Brown (1958) discussed this question at length, noting that adult speakers of a language typically converge on the names that they give to common objects or categories. Building on this work, Rosch and Lloyd (1978) proposed that many semantic domains have clear basic level categories, such as apple, fish or knife (See Fig 1 (a) for an example).