Large Language Models Explained Simply (In 13 Minutes) — Transcript

A clear 13-minute explanation of large language models, covering their data, architecture, training, and real-world applications.

Key Takeaways

Large language models rely on predicting tokens using vast amounts of text data.
Transformers and attention mechanisms are key innovations enabling contextual understanding.
Training is a massive computational process using gradient descent on billions of parameters.
These models enable AI to perform complex language tasks quickly and at scale.
They do not learn on the fly but operate based on pre-trained knowledge.

Summary

Large language models predict the next token in a sentence based on prior context using massive datasets.
The term 'large' refers to the enormous volume of text data these models are trained on, often hundreds of billions to over a trillion words.
Data primarily comes from the open web, including books, websites, Wikipedia, Reddit, and other public sources.
Tokens can be words, parts of words, or punctuation, which are converted into numerical embeddings representing meaning and context.
The core architecture is a neural network called a Transformer, which uses an attention mechanism to understand relationships between words.
Attention allows the model to focus on relevant parts of input, enabling it to grasp nuances and context across long sentences.
Training involves gradient descent, iteratively adjusting billions of parameters to improve prediction accuracy over millions or billions of cycles.
Large language models do not learn in real time but apply knowledge gained during extensive pre-training.
These models power AI systems like Chat-GPT, Google Gemini, and Claude, enabling tasks like writing, coding, and research instantly.
Despite their complexity and power, running these models requires significant computational resources and energy.

Full Transcript — Download SRT & Markdown

Speaker A

What are large language models? Well, to give you a very simple answer, a large language model is a type of AI that tries to predict the next word, or more accurately, the next token, in a sentence based on what came before it. Now you might be thinking, is that it?

Speaker A

That sounds almost too simple. And you'd be right in a sense…but as simple as it sounds, it is in the actual process of how language models makes those predictions, where the real complexity lies. Imagine this. You're texting a friend, who just happens to have a Marvel comics

Speaker A

That sounds almost too simple. And you'd be right in a sense… but as simple as it sounds, it is in the actual process of how language models make those predictions, where the real complexity lies. Imagine this. You're texting a friend, who just happens to have a Marvel comics

Speaker A

tomorrow you might ask him to help with your homework, or answer random questions, and no matter what, he always seems ready to give you an answer. That friend is basically a large language model. To give you the formal, less simple sounding definition:

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level ability to absorb everything he reads. I'm talking every single book, every website, every Wikipedia article, and every Reddit thread ever written. For the sake of it, let's say your

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Now, I want to focus on one key word, and that is the word, large. And boy, does it live up to its name. The word 'large' pertains to the absolutely massive amounts of data that these models are trained on. Some of the biggest language models have been trained on

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friend's name just happens to be Chat-GPT. Today you might text your friend to write you a story,

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of nonstop reading to get through it all. Even if you started reading when the first pyramids were built, you'd still be at it today. And it's not just the amount of text that's large.

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tomorrow you might ask him to help with your homework, or answer random questions, and no matter what, he always seems ready to give you an answer. That friend is basically

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does all this data come from? It's a place that we all frequent every single day, where trolls, haters, and know-it-alls come together to leave some kind of digital footprint: the internet. The largest portion of data comes from the open web, including all the publicly available text, blogs,

Speaker A

a large language model. To give you the formal, less simple sounding definition:

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Okay, so we know that large language models are massive. They've read more text than any human could even dream of, and they're complex enough to make your laptop spontaneously combust if you tried to run one on it. But how do they actually learn from all that data to sound…well, human? It

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large language models are advanced AI systems based on deep learning and transformer

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Think of it like asking the AI a question, giving it an instruction, or starting a conversation.

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architectures, trained on massive datasets to understand, summarize, generate, and predict

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These tokens can be full words, parts of words, or even punctuation marks, depending on the language model. For example, the sentence "Machine Learning is awesome", might get chopped into pieces like this. Each of those tokens is then translated into numbers, because ultimately, that's the only thing

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text and code. It's what powers Google Gemini, Claude, and of course, the famous Chat-GPT.

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embeddings like coordinates in a massive map of meaning. Words with similar meanings end up closer together, while totally unrelated words are far apart. So instead of seeing just words, or even raw numbers, the model is now working with vectors that capture context, relationships,

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Now, I want to focus on one key word, and that is the word, large. And boy,

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Once your text is tokenized and turned into these embeddings, it's ready to be fed into something called a neural network, which is essentially the model's brain…and where all the real magic happens. This is where we look at the actual architecture of a large language model. A neural

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does it live up to its name. The word 'large' pertains to the absolutely massive amounts of

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adds a bias (which is a little push to help it make better decisions) and then passes that result to the next layer. Multiply that process by billions of these neurons working together, and boom: that's the foundation of a large language model. I highly recommend you

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data that these models are trained on. Some of the biggest language models have been trained on

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And speaking of neural networks, large language models use a very specific type of neural network called a Transformer. No, not that kind of transformer. Sorry, Optimus. The type of transformer I'm talking about looks more like this. Yeah I know, maybe not as flashy looking on

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hundreds of billions to over a trillion words. To give you perspective on just how massive this is,

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or write a sentence, the meaning of a word often depends on other words far away in the sentence.

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if you had to read through all the words these models are trained on, for 24 hours a day,

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But depending on which word we emphasize, we can completely changes the meaning of the sentence.

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with no breaks, no sleep, or interruptions of any sort, it would take you roughly 7,600 years

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money". The emphasis on the word 'he' shifts the meaning to someone else being the thief.

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of nonstop reading to get through it all. Even if you started reading when the first pyramids

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in a very linear way, which meant that these AI models couldn't really deduce these nuances in language, the way humans can. That is, drum roll please! Until the arrival of transformers. They came and gave AI a similar ability to humans in terms being able to comprehend meaning between

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were built, you'd still be at it today. And it's not just the amount of text that's large.

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of the relevant context in a sentence at once. The key idea behind transformers is something called the attention mechanism. Here's a high level overview. The Attention Mechanism was introduced to the scene in a famous paper in 2017 called "Attention Is All You Need". This paper is pretty

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The actual models themselves are massive. Inside these models are billions, sometimes trillions of

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the sentence, "Machine Learning is awesome", the model pays extra attention to "machine learning" to understand what is being described as awesome. This ability to focus on relevant parts of the input - while ignoring the less important stuff is what allows large language models to understand

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tiny parameters, which are adjustable numbers that control how the model predicts language. And also,

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what it means using attention. The decoder's job is to take that understanding and turn it into new text, one token at a time. And if you've ever heard the term 'multi-headed attention', it just means the model is paying attention in several different ways at once. Also, when a language

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I should add that the term large certainly applies to the energy bill as well. So where

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Chat-GPT doesn't always give the exact same answer twice. It's not copying or recalling responses. It's sampling from probabilities. One last behind-the-scenes detail. During training, models learn using a method called gradient descent. The idea is actually pretty simple.

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does all this data come from? It's a place that we all frequent every single day, where trolls,

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This process is repeated millions of times, sometimes billions of times, during training. So what does this mean in plain English? When you're using Chat-GPT, it's not learning in real time. It's applying everything it already learned during training. If you want to go

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haters, and know-it-alls come together to leave some kind of digital footprint: the internet. The

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Large language models are powerful because they turn human language into something computers can work with at scale, reason with, and translate. This means that tasks that once required teams of people, from writing, to coding, to researching, can now happen instantly on demand.

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largest portion of data comes from the open web, including all the publicly available text, blogs,

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before. And honestly, that's why I like comparing model sometimes. If you’ve ever wondered between using ChatGPT, or Google Gemini, or Claude, why not have access to all of them on one platform?

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news articles, and forums such as Reddit. The data also comes from digitized books,

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where I break these ideas down even further — no heavy math, no jargon, just clear explanations. If you liked this video, please like and subscribe and leave a comment down below of what topic you'd like to see covered next. Thank you for watching, and I'll see you guys in the next video.

Topics:large language modelstransformersattention mechanismdeep learningChat-GPTnatural language processingAI traininggradient descenttokenizationneural networks

Frequently Asked Questions

What is a large language model?

A large language model is an AI system that predicts the next token in a sentence based on previous context, trained on massive amounts of text data.

Where do large language models get their training data?

They are trained primarily on publicly available text from the open web, including books, websites, Wikipedia, and forums like Reddit.

How do transformers improve language model performance?

Transformers use an attention mechanism that allows the model to focus on relevant parts of input text, capturing context and relationships across long sentences.

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