A Survey on Large Language Models from Concept to Implementation

TL;DR

This survey comprehensively reviews the development, applications, and impact of large language models based on Transformer architectures, emphasizing their versatility across NLP and other domains.

Contribution

It provides an in-depth overview of recent research on Transformer-based LLMs, highlighting their applications, advancements, and potential future directions.

Findings

Transformers have revolutionized NLP applications.

LLMs like GPT enable diverse tasks beyond traditional NLP.

Transformer models are key to future AI innovations.

Abstract

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.