Understanding models understanding language

Abstract Landgrebe and Smith (Synthese 198(March):2061–2081, 2021) present an unflattering diagnosis of recent advances in what they call language-centric artificial intelligence—perhaps more widely known as natural language processing: The models that are currently employed do not have sufficient expressivity, will generalize, fundamentally unable to induce linguistic semantics, say. is mainly derived from analysis the used Transformer architecture. Here I address a number misunderstandings their analysis, take be adequate ability learn semantics. To avoid confusion, distinguish between inferential referential (2021)’s architecture’s expressivity generalization concerns This part shown rely on technical properties Transformers. (2021) also claim semantics unobtainable for models. In response, non-technical discussion techniques grounding models, giving them even absence supervision. simple thought experiment highlight mechanisms would lead discuss sense grounded this way, can said understand language. Finally, approach advocate for, namely manual specification formal grammars associate expressions with logical form.