A Philosophical Introduction to Language Models -- Part I: Continuity
  With Classic Debates
Raphaël Millière Cameron Buckner
Large language models like GPT-4 have achieved remarkable proficiency in a
broad spectrum of language-based tasks, some of which are traditionally
associated with hallmarks of human intelligence. This has prompted ongoing
disagreements about the extent to which we can meaningfully ascribe any kind of
linguistic or cognitive competence to language models. Such questions have deep
philosophical roots, echoing longstanding debates about the status of
artificial neural networks as cognitive models. This article -- the first part
of two companion papers -- serves both as a primer on language models for
philosophers, and as an opinionated survey of their significance in relation to
classic debates in the philosophy cognitive science, artificial intelligence,
and linguistics. We cover topics such as compositionality, language
acquisition, semantic competence, grounding, world models, and the transmission
of cultural knowledge. We argue that the success of language models challenges
several long-held assumptions about artificial neural networks. However, we
also highlight the need for further empirical investigation to better
understand their internal mechanisms. This sets the stage for the companion
paper (Part II), which turns to novel empirical methods for probing the inner
workings of language models, and new philosophical questions prompted by their
latest developments.