Lesson 3: Training Large Language Models | A Compact Guide to Large Language Models pdf Download | Generative ai Examples | Llm Meaning Software | Turtles AI

Lesson 3: Training Large Language Models
  #Training is a vital part of creating an #effective LLM. In this lesson, we'll #discuss how #LLMs learn from vast amounts of text data. The process begins with a training #dataset, which is a large collection of text from various sources. The #LLM "reads" the text and tries to predict the next word in a sentence based on the words that came before it. This is called language modelling. The LLM's predictions are compared to the actual text, and any errors are used to adjust the connections between the neurons in the network (see the previous lesson on #neural networks). This process is repeated for millions or even billions of sentences, allowing the LLM to learn the intricacies of human (natural) language. As the model becomes more accurate, it can generate more coherent and contextually relevant text. This is known as fine-tuning, and it's an essential step in the development of a useful LLM. We could say that training a large language model is like teaching a baby (robot) how to talk. Just like how kids learn to talk by listening to their parents and teachers, an LLM learns how to talk by reading lots and lots of books, articles, and websites. As we said in the first lesson, it's like a giant library that the LLM goes through, trying to predict what the next word in a sentence will be. And just like how you learn from your mistakes, the LLM also learns from its errors and makes adjustments to get better at predicting words. Eventually, after reading millions or even billions of sentences, the LLM becomes good at talking and can generate (original) sentences that make sense.