What is a Large Language Model?
45sExplains the core concept in a simple, engaging way that hooks viewers curious about AI.
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GPT, or Generative Pre-trained Transformer, is a large language model (LLM) that generates human-like text. This video explains what an LLM is, how it works, and its business applications.
Chapters
[00:00]
Definition of GPT
GPT stands for Generative Pre-trained Transformer, a type of large language model (LLM) that generates human-like text.
[00:36]
LLMs as Foundation Models
Large language models are instances of foundation models, pre-trained on large amounts of unlabeled, self-supervised data to produce generalizable output.
[01:09]
Training Data Scale
LLMs are trained on large datasets like books and articles, potentially petabytes in size. A 1 GB text file stores about 178 million words.
[02:04]
Parameter Count
Parameters are values the model adjusts during learning. GPT-3 has 175 billion parameters and was trained on 45 terabytes of data.
[02:28]
Components of an LLM
An LLM consists of three components: data, architecture (neural network, specifically transformer for GPT), and training.
[03:03]
Transformer Architecture
Transformers handle sequences like sentences by understanding each word in context of every other word, building comprehensive sentence understanding.
[03:29]
Training Process
During training, the model learns to predict the next word in a sentence, adjusting parameters to reduce prediction error until it generates coherent text.
[04:02]
Fine-Tuning
Fine-tuning on a smaller, specific dataset allows a general LLM to become an expert at a specific task.
[04:23]
Business Applications
LLMs are used for customer service chatbots, content creation (articles, emails, social media), and software development (code generation and review).
Large language models like GPT are powerful tools for generating human-like text, with applications across customer service, content creation, and software development. As they evolve, more innovative uses will emerge.
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Study Flashcards (10)
What does GPT stand for?
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What does GPT stand for?
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Generative Pre-trained Transformer
What is a foundation model?
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What is a foundation model?
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A model pre-trained on large amounts of unlabeled, self-supervised data to produce generalizable output.
00:49
How many words can a 1 GB text file store?
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How many words can a 1 GB text file store?
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About 178 million words.
01:38
How many parameters does GPT-3 have?
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How many parameters does GPT-3 have?
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175 billion parameters.
02:20
What are the three components of an LLM?
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What are the three components of an LLM?
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Data, architecture, and training.
02:34
What architecture does GPT use?
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What architecture does GPT use?
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Transformer architecture.
02:55
How does a transformer understand context?
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How does a transformer understand context?
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By considering each word in relation to every other word in the sentence.
03:13
What is the training objective of an LLM?
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What is the training objective of an LLM?
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To predict the next word in a sentence.
03:29
What is fine-tuning?
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What is fine-tuning?
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Training a general LLM on a smaller, specific dataset to become an expert at a specific task.
04:02
Name three business applications of LLMs.
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Name three business applications of LLMs.
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Customer service chatbots, content creation, and software development.
04:23
💡 Key Takeaways
📊
Definition of GPT
Provides the foundational definition of the core topic.
📊
Scale of Training Data
Illustrates the massive scale of data used, emphasizing the 'large' in LLM.
01:31
📊
Parameter Count of GPT-3
Highlights the complexity and size of modern LLMs.
02:04
🔧
Transformer Architecture Explanation
Clearly explains how transformers understand context, a key technical insight.
03:03
💡
Business Applications
Connects technical concepts to real-world use cases, showing practical value.
04:23Full Transcript
[00:00] GPT, or Generative Pre-trained Transformer,
[00:03] is a large language model, or an LLM,
[00:08] that can generate human-like text.
[00:10] And I've been using GPT in its various forms for years.
[00:15] In this video we are going to number 1,
[00:18] ask "what is an LLM?"
[00:22] Number 2, we are going to describe how they work.
[00:26] And then number 3,
[00:28] we're going to ask, "what are the business applications of LLMs?"
[00:32] So let's start with number 1, "what is a large language model?"
[00:36] Well, a large language model
[00:40] is an instance of something else called a foundation model.
[00:49] Now foundation models are pre-trained on large amounts of unlabeled and self-supervised data,
[00:55] meaning the model learns from patterns in the data in a way that produces generalizable and adaptable output.
[01:01] And large language models are instances of foundation models applied specifically to text and text-like things.
[01:09] I'm talking about things like code.
[01:11] Now, large language models are trained on large datasets of text, such as books, articles and conversations.
[01:18] And look, when we say "large",
[01:21] these models can be tens of gigabytes in size
[01:24] and trained on enormous amounts of text data.
[01:27] We're talking potentially petabytes of data here.
[01:31] So to put that into perspective,
[01:33] a text file that is, let's say, one gigabyte in size,
[01:38] that can store about 178 million words.
[01:44] A lot of words just in one Gb.
[01:47] And how many gigabytes are in a petabyte?
[01:51] Well, it's about 1 million.
[01:57] Yeah, that's truly a lot of text.
[01:59] And LLMs are also among the biggest models when it comes to parameter count.
[02:04] A parameter is a value the model can change independently as it learns,
[02:08] and the more parameters a model has, the more complex it can be.
[02:11] GPT-3, for example, is pre-trained on a corpus of actually 45 terabytes of data,
[02:20] and it uses 175 billion ML parameters.
[02:25] All right, so how do they work?
[02:28] Well, we can think of it like this.
[02:30] LLM equals three things:
[02:34] data, architecture, and lastly, we can think of it as training.
[02:44] Those three things are really the components of an LLM.
[02:47] Now, we've already discussed the enormous amounts of text data that goes into these things.
[02:53] As for the architecture,
[02:55] this is a neural network and for GPT that is a transformer.
[03:03] And the transformer architecture enables the model to handle sequences of data
[03:07] like sentences or lines of code.
[03:09] And transformers are designed to understand the context of each word in a sentence
[03:13] by considering it in relation to every other word.
[03:17] This allows the model to build a comprehensive understanding of the sentence structure
[03:21] and the meaning of the words within it.
[03:23] And then this architecture is trained
[03:25] on all of this large amount of data.
[03:29] Now, during training, the model learns to predict the next word in a sentence.
[03:33] So, "the sky is..." it starts off with a with a random guess, "the sky is bug".
[03:41] But with each iteration, the model adjusts its internal parameters
[03:45] to reduce the difference between its predictions and the actual outcomes.
[03:50] And the model keeps doing this gradually improving its word predictions
[03:53] until it can reliably generate coherent sentences.
[03:57] Forget about "bug", it can figure out it's "blue".
[04:02] Now, the model can be fine tuned on a smaller, more specific dataset
[04:07] Here the model refines its understanding to be able to perform this specific task more accurately.
[04:13] Fine tuning is what allows a general language model
[04:16] to become an expert at a specific task.
[04:18] OK, so how does this all fit into number 3, business applications?
[04:23] Well, for customer service applications,
[04:27] businesses can use LLMs to create intelligent chatbots that can handle a variety of customer queries,
[04:33] freeing up human agents for more complex issues.
[04:37] Another good field, content creation.
[04:40] That can benefit from LLMs which can help generate articles,
[04:44] emails, social media posts, and even YouTube video scripts.
[04:49] Hmm, there's an idea.
[04:51] Now, LLMs can even contribute to software development.
[04:57] And they can do that by helping to generate and review code.
[05:00] And look, that's just scratching the surface.
[05:03] As large language models continue to evolve,
[05:05] we're bound to discover more innovative applications.
[05:09] And that's why I'm so enamored with large language models.
[05:14] If you have any questions, please drop us a line below.
[05:17] And if you want to see more videos like this in the future,
[05:20] please like and subscribe.
[05:22] Thanks for watching.