The Singleton Fallacy: Why Current Critiques of Language Models Miss the Point
This paper addresses a foundational philosophical debate about the nature of understanding in AI, relevant to researchers and developers working on NLU.
This paper argues that current critiques of language models suffer from the "singleton fallacy," assuming language, meaning, and understanding are singular phenomena. Instead, the authors propose that language models are designed to acquire and represent a specific type of structural understanding, which they contend is sufficient to be considered "real" understanding.
This paper discusses the current critique against neural network-based Natural Language Understanding (NLU) solutions known as language models. We argue that much of the current debate rests on an argumentation error that we will refer to as the singleton fallacy: the assumption that language, meaning, and understanding are single and uniform phenomena that are unobtainable by (current) language models. By contrast, we will argue that there are many different types of language use, meaning and understanding, and that (current) language models are build with the explicit purpose of acquiring and representing one type of structural understanding of language. We will argue that such structural understanding may cover several different modalities, and as such can handle several different types of meaning. Our position is that we currently see no theoretical reason why such structural knowledge would be insufficient to count as "real" understanding.