Exploring Large Language Models Architectures Applications, and Emerging Challenges

Abstract

This survey provides an in-depth exploration of Large Language Models (LLMs), examining notable architectures such as GPT-3, GPT-4, LLaMA, and PaLM. The paper traces the architectural evolution from traditional neural language models to cutting-edge transformer-based systems. Detailed insights are provided on training methodologies, including pre-training, fine-tuning, and instruction-tuning, which have enhanced the versatility and performance of LLMs across a range of applications, including natural language processing, text summarization, and code generation. This survey also discusses the current challenges LLMs face, such as bias in model outputs, ethical concerns, and the computational demands of scaling these models. Through analysis, we highlight the potential of LLMs to revolutionize industries while underscoring the need for efficient training techniques to mitigate their resource-intensive nature. Our findings indicate that while LLMs offer transformative capabilities, addressing ethical and practical limitations will be critical to their future development.