Bridging AI and Child Development: A Comparative Study of Hallucinations in LLMs and Children’s Cognitive Errors

Xue li, Xiaonan Song

Keywords: LLMs, AI hallucinations, developmental psychology, cognitive errors, AI ethics

TL;DR: By analyzing AI hallucinations through the lens of developmental psychology, this paper proposes new strategies to make large language models and video generation systems more reliable and trustworthy.

Abstract: This paper examines the inherent limitations of Large Language Models (LLMs) and text-to-video generation systems, focusing particularly on their propensity to generate outputs that are factually incorrect or semantically incoherent. We analyze these shortcomings through the framework of cognitive development in children, drawing parallels between the error patterns observed in AI systems and the cognitive errors prevalent in early childhood. Our central hypothesis is that insights from developmental psychology, specifically the strategies employed to correct falsehoods and misconceptions in children, can be adapted and applied to enhance the reliability and accuracy of LLMs and text-to-video systems. The research explores various mechanisms to improve AI outputs, with a significant emphasis on fostering transparency in AI decision-making processes and maintaining robust human oversight in the loop. By adopting a cross-disciplinary approach that bridges artificial intelligence and developmental psychology, this paper aims to contribute to the advancement of safer, more trustworthy, and ethically grounded AI technologies. The ultimate goal is to promote responsible AI development and deployment, addressing critical challenges related to misinformation, bias, and the potential for unintended consequences. This work underscores the importance of viewing AI systems not as infallible entities, but as tools that require careful calibration and continuous monitoring to ensure their alignment with human values and societal well-being.

Submission Number: 6