Meaning and understanding in large language models
Vladimír Havlík
Can a machine understand the meanings of natural language? Recent
developments in the generative large language models (LLMs) of artificial
intelligence have led to the belief that traditional philosophical assumptions
about machine understanding of language need to be revised. This article
critically evaluates the prevailing tendency to regard machine language
performance as mere syntactic manipulation and the simulation of understanding,
which is only partial and very shallow, without sufficient referential
grounding in the world. The aim is to highlight the conditions crucial to
attributing natural language understanding to state-of-the-art LLMs, where it
can be legitimately argued that LLMs not only use syntax but also semantics,
their understanding not being simulated but duplicated; and determine how they
ground the meanings of linguistic expressions.