The Flashcard Sorter - Applicability of the Chinese Room Argument to Large Language Models (Preprint)

Does the Chinese Room Argument (CRA) apply to large language models (LLMs)? The thought experiment at the center of the CRA is tailored to Good Old-Fashioned Artificial Intelligence (GOFAI) systems. However, natural language processing has made significant progress, especially with the emergence of LLMs in recent years. LLMs differ from GOFAI systems in their design; they operate on vectors rather than symbols and do not follow a program but instead learn to map inputs to outputs. Consequently, some have suggested that the CRA is no longer relevant in discussions surrounding artificial language understanding. Contrary to these authors, I argue that if the CRA successfully demonstrates that implementing a symbolic computation is not sufficient for language understanding, then it also shows that implementing an LLM is not sufficient for language understanding. At the core of my argument lies a thought experiment called “the flashcard sorter”.