【摘要】 在探索人工智能领域的无限可能性中，大模型的出现标志着一次重大技术飞跃．大模型通过大规模预训练，掌握了语言的复杂结构和多样性，展现了前所未有的语境理解和任务适应能力．然而，它们的涌现能力、对大量计算资源的依赖，以及在多任务处理中可能产生的不良内容等问题，仍是当下研究的关键挑战．本文综述了大模型的发展历程和技术背景，并深入探讨了它们在基础通用理论、智能交互方法和安全防护策略方面的最新进展，为未来大模型的科学研究和技术应用提供指导．更多还原

【Abstract】 In exploring the infinite possibilities in the field of artificial intelligence, the emergence of large language models(LLM) marks a significant technological leap. LLM, through massive-scale pre-training, have mastered the complex structures across diverse languages, demonstrating unprecedented contextual understanding and task adaptability. However, its emergent capabilities, dependence on extensive computational resources, and the issue of the likely generation of harmful content in multi-task processing remain key challenges for on-going researches. This paper reviews the development history and technical background of LLM and delves into its latest advancements in basic general theories, intelligent interaction methods, and security protection strategies. It provides valuable insights and guidance for future scientific research and technical applications of LLM.更多还原