Over the last decade, AI models of language and word meaning have been dominated by what we might call a statistics-of-occurrence, strategy: these models are deep neural net structures that have been trained on a large amount of unlabeled text with the aim of producing a model that exploits statistical information about word and phrase co-occurrence in order to generate behavior that is similar to what a human might produce, or representations that can be probed to exhibit behavior similar to what a human might produce (meaning-semblant behavior). Examples of what we can call Statistics-of-Occurrence Models (SOMs) include: Word2Vec (CBOW and Skip-Gram), BERT, GPT-3, and, most recently, ChatGPT. Increasingly, there have been suggestions that such systems have semantic understanding, or at least a proto-version of it. This paper argues against such claims. I argue that a necessary condition for a system to possess semantic understanding is that it function in ways that are causally explainable by appeal to its semantic properties. I then argue that SOMs do not plausibly satisfy this Functioning Criterion. Rather, the best explanation of their meaning-semblant behavior is what I call the Statistical Hypothesis: SOMs do not themselves function to represent or produce meaningful text; they just reflect the semantic information that exists in the aggregate given strong correlations between word placement and meaningful use. I consider and rebut three main responses to the claim that SOMs fail to meet the Functioning Criterion. The result, I hope, is increased clarity about why and how one should make claims about AI systems having semantic understanding.

在过去的十年中，语言和词义的人工智能模型一直由我们所谓的发生统计策略主导：这些模型是深度神经网络结构，已经在大量未标记的文本上进行了训练，其目的是生成一个模型，该模型利用有关单词和短语共现的统计信息来生成与人类可能产生的行为类似的行为，或者可以探测到表现出与人类可能产生的行为类似的表示（意义相似）行为）。我们所说的统计发生模型 (SOM) 的示例包括：Word2Vec（CBOW 和 Skip-Gram）、BERT、GPT-3 以及最近的 ChatGPT。越来越多的人认为此类系统具有语义理解，或者至少是其原始版本。本文反对这种说法。我认为，一个系统拥有语义理解的必要条件是，它的运作方式可以通过诉诸其语义属性进行因果解释。然后我认为 SOM 似乎无法满足这个功能标准。相反，对它们的意义相似行为的最好解释就是我所说的统计假设：SOM 本身并不用于表示或生成有意义的文本；它们只是反映了总体中存在的语义信息，因为词的位置和有意义的使用之间存在很强的相关性。对于 SOM 未能满足功能标准的说法，我考虑并反驳了三个主要回应。我希望，结果可以让人们更加清楚地了解为什么以及如何宣称人工智能系统具有语义理解能力。