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 attribute to other humans because they have 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.

翻译：当前大型语言模型在生成语法正确、流畅的文本方面展现出前所未有的能力。随着大型语言模型的快速涌现，关于其能力的辩论迅速展开，但相应的反思却相对滞后。因此，这篇立场论文首先聚焦于该辩论，并批判性评估了针对大型语言模型能力的三个反复出现的论点：其一，认为大型语言模型仅能模仿训练数据中的统计模式；其二，认为大型语言模型掌握语言的形式能力而非功能能力；其三，认为大型语言模型的语言习得无法为人类语言学习提供启示。通过结合经验证据与理论论证，我们揭示了这些论点需要更多精细化考量。其次，我们针对大型语言模型"真正"理解力与意向性问题提出一个实用主义视角。理解与意向性涉及不可观测的心理状态——这些概念之所以被归因于其他人类，是因为其具有实用价值：它们使我们能够忽略复杂的底层机制，有效预测行为。我们进一步探讨了在何种条件下，人类类似地将心理状态归因于大型语言模型具有合理性，从而为这一日益重要的社会技术构建实用主义哲学语境。