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.

基于 Transformer 架构的大型语言模型（LLMs）的近期进展在自然语言处理（NLP）应用领域展示了显著的拓宽范围，超越了其在聊天机器人技术中的初始应用。本文探究了这些模型的多方面应用，重点关注 GPT 系列，对人工智能（AI）驱动工具在改变编码、问题解决等传统任务上起到的转变性影响进行了研究，同时开辟了在不同行业中进行研究和开发的新方向。从代码解释和图像描述到便于构建交互式系统和推动计算领域的发展，Transformer 模型展示了深度学习、数据分析和神经网络设计相结合的协同作用。本调查报告深入了解了 Transformer 模型的最新研究，突出了其多功能性和在实际应用领域中转型的潜力，从而为读者提供了对 Transformer-based LLMs 在当前和未来实际应用领域的全面理解。