LLMs and the Problem of Uncommon Ground

Following the Symbol Grounding Problem, it is generally held that any natural language system, in order to produce meaningful outputs, must depend on some additional system or ground. Given their near-flawless linguistic performance, some have suggested that Large Language Models (LLMs) may possess a form of semantic “understanding” and therefore must be grounded in some respect. With this project, however, I avoid taking a position as to whether LLMs are grounded; rather, I point out that LLMs cannot be sensorimotor grounded, or grounded in motor and sensory responses, which is the fundamental ground of the human language system. I support this claim by drawing on research in neurosemiotics and embodied semantics, which indicates that at its core, human semantic content is rooted in worldly interactions and sensory responses, which are unavailable to disembodied LLMs. Thus, even if LLMs are grounded in some way, they will be differently grounded from the human language system, and this uncommon ground ultimately presents a persistent barrier that prevents both language systems from achieving reliable compatibility and mutual understanding.