Language and Thought: The View from LLMs

TL;DR

This paper argues that large language models demonstrate that language fundamentally transforms thought, making inference more efficient and suggesting language's crucial role in human cognition.

Contribution

It provides a philosophical and empirical analysis linking LLM performance to Dennett's thesis on language's impact on mind and cognition.

Findings

LLMs support Dennett's view on language and thought

Linguistic encoding enhances inference efficiency in AI

Implications for understanding human cognition and language

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.