Large Language Models (LLMs) like GPT-4 are advanced AI systems designed to understand and generate human-like text based on the input they receive. LLMs are trained on vast datasets of text from the internet, which enables them to respond to a wide range of queries with informative and contextually relevant answers. This training allows them to mimic human-like understanding and writing styles. The usefulness of LLMs lies in their versatility: they can assist you in tasks ranging from writing and summarizing to more complex reasoning and problem-solving.
Think of an LLM as a versatile kitchen blender. Just as a blender takes various ingredients and combines them to create smoothies, soups, or sauces, an LLM takes in a vast array of information (text) and blends it to produce coherent, contextually relevant responses. The blender's ability to handle different ingredients and create a variety of mixtures is akin to the LLM's capacity to process diverse topics and generate answers, stories, or even creative content. The quality of the output (a smoothie or an answer) depends significantly on the quality and variety of the input (ingredients or training data).
Surprisingly, language models don't actually understand language. They just learn to predict the next word in a sequence of words. This is done by transforming words into numbers, which are then fed into the model as input. The process of transforming words into numbers is called tokenization. Let's take a look at how this works in practice.