Colin Brown explains how large language models work: they're trained on billions of words from the web and books to predict the next word in a sequence. He covers ChatGPT, Claude, Bard, and dozens of others, then walks through real problems like hallucinations, bias, and the massive energy cost of training them.
Everyone's heard of or played with ChatGPT. You ask it a question, it provides an answer. You may have wondered, what does GPT stand for? Stands for Generative Pre-trained Transformer. Is it important to know that? Not really. It does use a large language model, also known as an LLM. So let's dive in and talk about LLMs for a minute. LLMs, or large language models, are advanced AI systems that have been trained on a large amount of data to recognize patterns, to understand and generate and manipulate human language. A large language model is an incredibly well-read digital assistant. Imagine if you could read every book, newspaper, and website in the world. An LLM does something similar by learning from huge amounts of text. It doesn't understand things the way humans do, but it recognizes patterns in language. When you ask it a question or give it a prompt, it predicts and generates a response based on the patterns it has learned. Essentially, it's a tool that helps computers generate human-like text by guessing what words should come next, much like a supercharged version of the autocomplete on your phone. How do LLMs work? Because they've been trained with large amounts of data, it can learn context to some degree and can learn to predict the next most probable word in a sentence. How do you train an LLM? Even though the model produces text that sounds like it understands, it's really just recognizing patterns. It doesn't have thoughts or feelings. It's essentially an advanced pattern matching tool. It works by comparing the input you give it to the vast amount of text it has already seen. In the training process, the model is given parts of sentences with some words missing. It then tries to predict the missing words. When it gets them right, it adjusts its internal settings to be even more accurate next time. Over many, many examples, it fine-tunes its ability to generate text that follows the natural flow of language. Because of this extensive training, the LLM can generate text that is surprisingly human-like. It can answer questions, write stories, or even hold conversations by piecing together words in a way that sounds natural to us. A large portion of the data comes from crawling and scraping the web, books and articles, public databases, and other public texts like social media. In essence, the training process involves gathering a huge, diverse set of texts, from many different sources, cleaning and organizing it, and then letting the model learn the patterns and structures of language from that data. Here are some examples of other popular LLMs that exist today. ChatGPT that comes from OpenAI. There's Copilot developed by Microsoft, which also runs using the backend of OpenAI. But if you were to ask Copilot if it runs on OpenAI, it will tell you that it does not and that it has its own training. Anthropic produced Claude. Google produced Bard. Meta produced Llama. And then we have DeepSeq coming from the Chinese. And then X has produced Groq. But how many LLMs are in existence today? There are over a million LLMs, and it grows daily. If you want to keep tabs, you can check out Hugging Face and the link there. There are some problems with LLMs. LLMs learn from large datasets that reflect human language, which can include biased or prejudiced viewpoints. This means the models may unintentionally reproduce or even amplify these biases in their outputs. LLMs sometimes generate information that sounds plausible but is entirely incorrect or even made up. This is often referred to as hallucinations and can be problematic. LLMs do not truly understand the meaning behind the words. They operate by recognizing patterns rather than reasoning about the content, which can lead to mistakes in context or logic. If certain topics are underrepresented or misrepresented in the data, the model might not perform well on those topics, leading to skewed or incomplete information. And since LLMs are trained on vast amounts of publicly available text, there are concerns about whether all the data was collected and used in a way that respects privacy and copyright laws. There's another problem, and that is energy consumption. Training these large models requires significant computational resources, which can have a considerable environmental impact due to high energy consumption. Ensuring that LLMs do not generate harmful or misleading content remains a major challenge. Developers have implemented safeguards, but it's an ongoing area of research to improve control over outputs. While AI does come with challenges like bias and occasional inaccuracies, its benefits and potential applications make it a fascinating and valuable field to dive into. Whether you're aiming to build a career in technology or just want to leverage AI as a tool in your field, there's a lot to gain from understanding how it works. If you want to learn more, you can check out our free AI resource library. Thanks for watching.
