The rapid advancement of Large Language Models (LLMs), exemplified by GPT-4, has transformed the landscape of artificial intelligence (AI) and natural language processing (NLP). These models have achieved remarkable humanlike fluency in generating coherent and contextually relevant text, raising both excitement and critical scrutiny within academia and industry [2]. Despite their linguistic prowess, fundamental questions remain about the nature of their “understanding.” Scholars highlight the tension between their statistical pattern-matching capabilities and the absence of deeper conceptual or experiential grounding, which is essential for truly understanding human language [4, 6]. This distinction is particularly salient when LLMs attempt tasks involving nuanced cultural, historical, or embodied contexts that are inherently tied to human lived experiences.
