Disembodied Meaning? Generative AI and Understanding

This study explores the cognitive and philosophical implications of Large Language Models (LLMs), focusing on their ability to generate meaning without embodiment. Grounded in the coherence-based semantics framework, the research challenges traditional views that emphasize the necessity of embodied cognition for meaningful language comprehension. Through a theoretical and comparative analysis, this paper examines the limitations of embodied cognition paradigms, such as the symbol grounding problem and critiques like Searle’s Chinese Room, and evaluates the practical capabilities of LLMs. The methodology integrates philosophical inquiry with empirical evidence, including case studies on LLM performance in tasks such as medical licensing exams, multilingual communication, and policymaking. Key findings suggest that LLMs simulate meaning-making processes by leveraging statistical patterns and relational coherence within language, demonstrating a form of operational understanding that rivals some aspects of human cognition. Ethical concerns, such as biases in training data and societal implications of LLM applications, are also analyzed, with recommendations for improving fairness and transparency. By reframing LLMs as disembodied yet effective cognitive systems, this study contributes to ongoing debates in artificial intelligence and cognitive science. It highlights their potential to complement human cognition in education, policymaking, and other fields while advocating for responsible deployment to mitigate ethical risks.