Can a machine understand the meanings of natural language? Recent developments in the generative large language models (LLMs) of artificial intelligence have led to the belief that traditional philosophical assumptions about machine understanding of language need to be revised. This article critically evaluates the prevailing tendency to regard machine language performance as mere syntactic manipulation and the simulation of understanding, which is only partial and very shallow, without sufficient referential grounding in the world. The aim is to highlight the conditions crucial to attributing natural language understanding to state-of-the-art LLMs, where it can be legitimately argued that LLMs not only use syntax but also semantics, their understanding not being simulated but duplicated; and determine how they ground the meanings of linguistic expressions.

翻译：机器能否理解自然语言的意义？人工智能领域生成式大型语言模型（LLMs）的最新发展，促使人们认为关于机器理解语言的传统哲学假设需要修正。本文批判性地评估了一种普遍倾向，即认为机器语言表现仅仅是句法操作和理解模拟，这种理解仅是局部且非常浅层的，缺乏充分的世界指涉基础。本文旨在揭示将自然语言理解归因于最先进LLMs的关键条件，在这些条件下，可以合理地论证LLMs不仅使用句法，还使用语义，其理解并非模拟而是复制；并确定它们如何为语言表达的意义提供指涉基础。