Self-attention mechanism is an integral component of modern machine learning models such as the Transformers, widely used in natural language processing tasks. It facilitates an understanding of the structure and semantics of the data by allowing models to “pay attention” to specific parts of the input while processing the data. However, explaining this sophisticated concept in simple terms can be a challenge. Let’s try to break it down. Understanding Self-Attention Mechanism Think of self-attention as if you were reading a novel. While reading a page, your brain doesn’t process each word independently. Instead, it understands the context by relating words […]
A Simplified Dive into Language Models: The Case of GPT-4
Introduction Language models have revolutionized the way we interact with machines. They have found applications in various fields, including natural language processing, machine translation, and even in generating human-like text. One of the most advanced language models today is GPT-4, developed by OpenAI. This blog post aims to provide a simplified deep dive into GPT-4, exploring its purpose, use cases, architecture, mechanism, limitations, and future prospects. Purpose of GPT-4 GPT-4, or Generative Pretrained Transformer 4, is a state-of-the-art autoregressive language model that uses deep learning to produce human-like text. It’s the latest iteration in the GPT series, and its primary […]