<aside> 🤖 Here’s a breakdown written BY an LLM, ChatGPT:
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Imagine trying to create a recipe from all the cooking shows you've ever watched. Your brain sifts through everything you've learned, combining bits of information to help you make a dish. Large Language Models (LLMs) work similarly, but in the world of words and information.
An LLM is a type of artificial intelligence that reads and absorbs vast amounts of text from the internet, books, articles—virtually anywhere you find words. It's like an incredibly well-read friend who's digested more books and sites than you could in a lifetime.
But what does an LLM do with all this information? It learns the art of conversation, writing, and even understanding human language. When you ask an LLM a question or seek its assistance in writing, it isn't pulling responses from thin air. Instead, it recalls all the text it's studied, understanding the context and the way language is used, to provide a response that makes sense.
So, LLMs are not creators of new ideas but rather expert communicators. They synthesize everything they've "read" and assist us in various tasks—from writing emails, generating reports, to answering complex questions, making our digital communications smoother and more efficient.
At its base, a language model is a program that uses a probability-based algorithm to decide what words it should use to reply to its input (often called a “prompt”), based on its previous training.
A large language model, or LLM for short, is a powerful language model that has been trained on a massive amount of data in order to accurately understand any input and respond accordingly.
<aside> 🗒️ How does a language model use probability to decide how it should respond to our messages? By training itself on example data, which can resemble anything from an article to someone’s social media message history, language models start to see a trend in what words follow others. Thus, when a language model takes in our messages, it calculates what words it would be most likely to see as a response based on its previous experience, and gives us that response. The fact that LLMs can respond to just about anything is a testament to just how much data they’ve trained on - no matter how uncommon your question might be, they will generally have a pretty good guess as to how to respond.
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If you ask ChatGPT what information it was trained on in order to be able to converse with humans, it won’t hesitate to tell you: in its training process, ChatGPT has taken in information and human interactions from all kinds of books, articles, papers, and websites. It also has the bandwidth to remember a large portion of that data - since ChatGPT does not have a live connection to the internet, all the questions it is able to answer are from its own training. Whenever we ask an LLM to do something, it is able to use the unimaginable amount of data at its disposal to decide exactly how to respond in a way that helps us the most. No other tool in human history has been able to aggregate and make use of this much information in a way that can be utilized as easily as sending a message.
Source: https://www.linkedin.com/pulse/corpus-used-large-language-models-llms-different-ankit-pareek/
Because LLMs are trained on all types of data, it is also much more flexible than any other machine learning program on the market. LLMs are capable of responding to our requests in any way we choose: we can tell one to give us only one-word answers, or structure its output in a table format, or even output code. This allows us to incorporate LLMs into our code and use their flexibility to our advantage, so that a program that would take an intensive amount of computer logic or formatting can instead be tackled as a no-code solution by requesting an LLM to process it. An LLM also takes a much more human approach to problems that we give it, which makes the resulting program much more robust - an oddly written user input or even a malicious attempt to break normal code with an malformed input doesn’t pose a problem to an LLM.
