A Survey on Large Language Models from Concept to Implementation
Chen Wang Jin Zhao Jiaqi Gong
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.