Exploring the Causes of Hallucinatory Phenomena and Coping Strategies in the Big Language Model

Large Language Models (LLMs) have demonstrated strong generative power in the field of natural language processing, but the “hallucination phenomenon” seriously affects the reliability and application security of the models. This paper systematically reviews the definition, categorization, causes and coping strategies of the hallucination phenomenon. It is found that hallucinations are mainly categorized into factual hallucinations and fidelity hallucinations. The root causes of these hallucinations are multifaceted, and they are related to data bias, model architecture defects, and limitations of the training process. To address these problems, this paper summarizes the coping methods such as data enhancement, introduction of external knowledge base and integrated strategies. Future research should focus on exploring feasible methods, including the application of quantum computing, robust model design, and interdisciplinary cooperation, to address hallucination challenges. The aim of this paper is to provide researchers and practitioners with a theoretical basis and practical guidance for addressing the phenomenon of hallucination.