Recent advancements in Large Language Models (LLMs), such as ChatGPT and LLaMA, have significantly transformed Natural Language Processing (NLP) with their outstanding abilities in text generation, summarization, and classification. Nevertheless, their widespread adoption introduces numerous challenges, including issues related to academic integrity, copyright, environmental impacts, and ethical considerations such as data bias, fairness, and privacy. The rapid evolution of LLMs also raises concerns regarding the reliability and generalizability of their evaluations. This paper offers a comprehensive survey of the literature on these subjects, systematically gathered and synthesized from Google Scholar. Our study provides an in-depth analysis of the risks associated with specific LLMs, identifying sub-risks, their causes, and potential solutions. Furthermore, we explore the broader challenges related to LLMs, detailing their causes and proposing mitigation strategies. Through this literature analysis, our survey aims to deepen the understanding of the implications and complexities surrounding these powerful models.

本研究解决了大型语言模型（LLMs）广泛应用所带来的众多挑战，包括学术诚信、版权和伦理问题等，填补了相关文献的空白。通过系统梳理和分析现有文献，我们提出了对特定LLMs的风险分类、成因及解决方案，以及更广泛的挑战和缓解策略。本研究的主要发现是这些模型的复杂性和潜在影响需要更深入的理解与应对。

大型语言模型（LLMs）广泛部署的风险、原因和缓解措施：一项综述