This talk raises the question of whether the Chinese Room Argument (CRA) applies 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. I begin by presenting a thought experiment called “the flashcard sorter”, which resembles the Chinese Room thought experiment but is tailored to LLMs rather than GOFAI systems. I then discuss the analogy between the flashcard sorter and modern LLMs, along with four objections to my claim that the CRA applies to LLMs. I conclude by presenting a modified version of the CRA.