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Current Large Language Models (LLMs) are unparalleled in their ability togenerate grammatically correct, fluent text. LLMs are appearing rapidly, anddebates on LLM capacities have taken off, but reflection is lagging behind.Thus, in this position paper, we first zoom in on the debate and criticallyassess three points recurring in critiques of LLM capacities: i) that LLMs onlyparrot statistical patterns in the training data; ii) that LLMs master formalbut not functional language competence; and iii) that language learning in LLMscannot inform human language learning. Drawing on empirical and theoreticalarguments, we show that these points need more nuance. Second, we outline apragmatic perspective on the issue of `real' understanding and intentionalityin LLMs. Understanding and intentionality pertain to unobservable mental stateswe attribute to other humans because they have pragmatic value: they allow usto abstract away from complex underlying mechanics and predict behavioureffectively. We reflect on the circumstances under which it would make sensefor humans to similarly attribute mental states to LLMs, thereby outlining apragmatic philosophical context for LLMs as an increasingly prominenttechnology in society.

Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding