Large Language Models Understanding: an Inherent Ambiguity Barrier
This addresses the debate on whether LLMs can truly understand language, which is important for AI researchers and philosophers, but it is incremental as it adds a counter-argument to existing discussions.
The paper argues that large language models (LLMs) face an inherent ambiguity barrier that prevents them from understanding the meaning of dialogues, based on a thought experiment and semi-formal considerations.
A lively ongoing debate is taking place, since the extraordinary emergence of Large Language Models (LLMs) with regards to their capability to understand the world and capture the meaning of the dialogues in which they are involved. Arguments and counter-arguments have been proposed based upon thought experiments, anecdotal conversations between LLMs and humans, statistical linguistic analysis, philosophical considerations, and more. In this brief paper we present a counter-argument based upon a thought experiment and semi-formal considerations leading to an inherent ambiguity barrier which prevents LLMs from having any understanding of what their amazingly fluent dialogues mean.