Hallucinations in Large Language Models: Cognitive Paradoxes, Innovative Value, and Rational Perspectives

ABSTRACT

Hallucinations in Large Language Models (LLMs) have emerged as a primary concern in both academic and industrial circles. This discrepancy between their outputs and objective facts is frequently labeled as a technical flaw, prompting widespread apprehension regarding their practical applications. However, the perception that equates hallucinations purely with "erroneous outputs" is rather limited. By integrating findings from cognitive science with the evolutionary trajectory of human civilization, hallucinations are essentially a natural outcome of intelligent agents breaking through existing knowledge boundaries and reconfiguring information connections. This article systematically synthesizes the definition, classification, and generation mechanisms of LLMs hallucinations. Through a comparison of the shared characteristics between “creative hallucinations” in human cognition and those observed in LLMs—and drawing on real-world precedents such as scientific revolutions, technological innovations, and groundbreaking achievements in ophthalmology—it argues that hallucinations are not inherently negative. Instead, they possess considerable potential for cross-domain knowledge integration, serving as inspiration for disruptive innovations, and facilitating the expansion of cognitive dimensions. This potential shares an isomorphic logic with the “paradoxical truths” and “groundbreaking hypotheses” that have historically driven the great leaps of human civilization. hallucinations as a “fundamental flaw” inherent to these