LLMs and the Logical Space of Reasons

Abstract Can Large Language Models (LLMs), such as ChatGPT, be considered genuine language users? Can they truly understand the meanings of natural language? This paper adopts an inferentialist perspective, arguing that grasping the meaning of an expression is nothing but grasping the inferential role the expression plays. But roles are conferred by rules . An expression’s contentfulness consists of its use being governed by inferential rules . Meaningful items incorporate norms of inference, which they are subject to. Thus, grasping meanings is mastering this bundle of rules. The paper questions whether LLMs, despite their ability to generate coherent text and perform inference-like tasks, follow such rules. However, unlike humans, LLMs are trained to detect statistical patterns . While it might be suggested that LLMs ‘know’ which words typically follow others based on statistical patterns, the norms they learn to master differ fundamentally from the norms of inference that govern language use. The paper concludes that the current version of LLMs, despite their advanced language processing capabilities, do not genuinely grasp or understand conceptual content and should, therefore, be viewed as simulations of language users rather than true participants in the logical space of reasons.