Large Language Models (LLMs) such as GPT-style transformers have reignited debates on the nature of thinking and whether these systems genuinely think or merely simulate thought. Blending technical and philo- sophical analysis, this paper assesses LLMs against criteria drawn from functionalism, the Chinese-Room argument, Chalmers’ “easy/hard” dis- tinction, and the symbol-grounding problem. We update the discussion with recent work that frames LLMs as mimics, not minds and foreground the often n…
Read moreLarge Language Models (LLMs) such as GPT-style transformers have reignited debates on the nature of thinking and whether these systems genuinely think or merely simulate thought. Blending technical and philo- sophical analysis, this paper assesses LLMs against criteria drawn from functionalism, the Chinese-Room argument, Chalmers’ “easy/hard” dis- tinction, and the symbol-grounding problem. We update the discussion with recent work that frames LLMs as mimics, not minds and foreground the often neglected question of agency: current models pursue no in- trinsic goals and act only when prompted. After outlining transformer architectures, pre-training, and in-context learning, we examine original- ity, intentionality, grounding, and autonomy to ask whether a convincing simulation ever suffices for real thought. We conclude that today’s LLMs powerfully simulate aspects of mind while lacking the conscious aware- ness, intrinsic intentionality, and self-directed agency that would justify calling them full-fledged thinkers.
