Language and Thought: The View from LLMs

TL;DR

Language shapes thought and enables domain-general inference by providing a compressed, abstract medium for representation. The Great AI Experiment with LLMs shows that extensive linguistic exposure yields broad inferential capabilities unmatched by non-language AI, suggesting that language makes general reasoning tractable. While not claiming AGI, the results illuminate how linguistic structure and data efficiency can transform cognitive performance, with implications for understanding human cognition and its development. The work positions language as a potential driver of cognitive transformation, though it cautions against overgeneralizing AI findings to biological minds.

Abstract

Daniel Dennett speculated in *Kinds of Minds* 1996: "Perhaps the kind of mind you get when you add language to it is so different from the kind of mind you can have without language that calling them both minds is a mistake." Recent work in AI can be seen as testing Dennett's thesis by exploring the performance of AI systems with and without linguistic training. I argue that the success of Large Language Models at inferential reasoning, limited though it may be, supports Dennett's radical view about the effect of language on thought. I suggest it is the abstractness and efficiency of linguistic encoding that lies behind the capacity of LLMs to perform inferences across a wide range of domains. In a slogan, language makes inference computationally tractable. I assess what these results in AI indicate about the role of language in the workings of our own biological minds.