Recent advancements in Large Language Models (LLMs), particularly those built on Transformer architectures, have significantly broadened the scope of natural language processing (NLP) applications, transcending their initial use in chatbot technology. This paper investigates the multifaceted applications of these models, with an emphasis on the GPT series. This exploration focuses on the transformative impact of artificial intelligence (AI) driven tools in revolutionizing traditional tasks like coding and problem-solving, while also paving new paths in research and development across diverse industries. From code interpretation and image captioning to facilitating the construction of interactive systems and advancing computational domains, Transformer models exemplify a synergy of deep learning, data analysis, and neural network design. This survey provides an in-depth look at the latest research in Transformer models, highlighting their versatility and the potential they hold for transforming diverse application sectors, thereby offering readers a comprehensive understanding of the current and future landscape of Transformer-based LLMs in practical applications.

翻译：近年来，基于Transformer架构的大型语言模型（LLMs）取得了显著进展，极大地拓展了自然语言处理（NLP）的应用范围，其影响已超越最初在聊天机器人技术中的应用。本文以GPT系列模型为重点，探讨了这些模型的多方面应用。研究聚焦于人工智能（AI）驱动工具对编码和问题求解等传统任务的革命性变革影响，同时揭示了其在跨行业研发中开辟的新路径。从代码解释、图像描述到促进交互系统构建以及推动计算领域发展，Transformer模型展现了深度学习、数据分析和神经网络设计的协同效应。本综述深入探讨了Transformer模型的最新研究，强调了其多功能性以及在变革各应用领域方面所具备的潜力，从而为读者全面理解基于Transformer的LLMs在当前及未来实际应用中的发展图景提供了系统性的视角。