Large Language Models: A Comprehensive Exploration of Modern AI's Potential and Pitfalls

Abstract

This paper presents a comprehensive exploration of Large Language Models(LLMs) and their significance in the quest for General Artificial Intelligence(GAI). Tracing the evolution of AI from its early symbolic approaches to modern data-driven, deep learning methods, the paper highlights how milestones in neural network architectures and computational resources have propelled the capabilities of LLMs. Key innovations such as transformers, self-attention mechanisms, and advanced training strategies are discussed. The paper also delves into the theoretical foundations, including various neural network types and the principles underlying their operation. Challenges such as bias, ethical concerns, and the computational demands of large-scale neural networks are addressed, alongside the potential of spiking neural networks(SNNs) and neuromorphic computing for future advancements.