Current Large Language Models (LLMs) are unparalleled in their ability to
generate grammatically correct, fluent text. LLMs are appearing rapidly, and
debates on LLM capacities have taken off, but reflection is lagging behind.
Thus, in this position paper, we first zoom in on the debate and critically
assess three points recurring in critiques of LLM capacities: i) that LLMs only
parrot statistical patterns in the training data; ii) that LLMs master formal
but not functional language competence; and iii) that language learning in LLMs
cannot inform human language learning. Drawing on empirical and theoretical
arguments, we show that these points need more nuance. Second, we outline a
pragmatic perspective on the issue of `real' understanding and intentionality
in LLMs. Understanding and intentionality pertain to unobservable mental states
we \textit{attribute} to other humans because they have \textit{pragmatic
value}: they allow us to abstract away from complex underlying mechanics and
predict behaviour effectively. We reflect on the circumstances under which it
would make sense for humans to similarly attribute mental states to LLMs,
thereby outlining a pragmatic philosophical context for LLMs as an increasingly
prominent technology in society.

当前大型语言模型（LLMs）在生成符合语法、流畅的文本方面无与伦比。这篇论文针对 LLMs 的能力进行了辩论，并通过批判性评估三个经常在批评中出现的观点来展示 LLMs 仍需更多细化。其次，文章从实证和理论的角度提出了对 LLMs 中 “真正” 的理解和意向性的实用观点，并讨论了在何种情况下将心理状态归因于 LLMs 对于这一日益重要的技术在社会中具有实用的哲学背景。