Part 1 - Unveiling the Challenges of Large Language Models (LLMs)
Introduction Large Language Models (LLMs) have emerged as a cornerstone in natural language processing (NLP). Their ability to generate human-like text, perform summarizations, and answer queries has revolutionized AI-driven applications. However, despite their transformative power, LLMs have inherent limitations that hinder their full potential in real-world scenarios. This blog delves into the mathematical underpinnings of LLMs, explores their challenges, and discusses avenues for improvement. Mathematical Formulation of LLMs Overview of Transformer Architectures LLMs are built upon the transformer architecture, which processes input sequences in parallel rather than sequentially. The primary innovation within transformers is the self-attention mechanism , which allows models to weigh the importance of words in a sentence relative to one another. Key Equation: Self-Attention Mechanism The self-atten...
