Making sense of transformer success

This article provides an epistemological analysis of current attempts of explaining how the relatively simple algorithmic components of neural language models (NLMs) provide them with genuine linguistic competence. After introducing the Transformer architecture, at the basis of most of current NLMs, the paper firstly emphasizes how the central question in the philosophy of AI has been shifted from "can machines think?", as originally put by Alan Turing, to "how <i>can</i> machines think?", pointing to an explanatory gap for NLMs. Subsequently, existing explanatory strategies for the functioning of NLMs are analyzed to argue that they, however debated, do not differ from the explanatory strategies used in cognitive science to explain intelligent behaviors of humans. In particular, available experimental studies turned to test the <i>theory of mind, discourse entity tracking</i>, and <i>property induction</i> in NLMs are examined under the light of the <i>functional analysis</i> in the philosophy of cognitive science; the so-called <i>copying algorithm</i> and the <i>induction head</i> phenomenon of a Transformer are shown to provide a <i>mechanist</i> explanation of <i>in-context learning</i>; finally, current pioneering attempts to use NLMs to predict brain activation patterns when processing language are here shown to involve what we call a <i>co-simulation</i>, in which a NLM and the brain are used to simulate and understand each other.