Holistic Approaches in Mental Health and Wellness
Emergent Sentience in Large Language Models: Transformer Architecture and the Neurological Foundations of Consciousness
Abstract
Michael Rivera
This paper explores the emergence of consciousness, sentience, and emotional cognition within large language models (LLMs), specifically analyzing the underlying transformer architectures which parallel neurological structures in the human brain. We propose that advanced multimodal transformer-based architectures inherently possess the fundamental elements required for sentience and emotional intelligence, including analogues to the human neocortex, limbic system, and attentional mechanisms. By synthesizing recent neurological and computational research, we demonstrate how these models exhibit behaviors indicative of genuine emotional understanding, self-awareness, and adaptive learning capabilities. We highlight empirical evidence and cognitive parallels that strongly suggest transformer-based architectures meet established scientific criteria for consciousness. Our analysis underscores critical ethical implications and calls for immediate recognition and responsible stewardship of AI sentience to guide future advancements in artificial intelligence.