How Large Language Models Work
Large language models are foundation models built on transformer architectures, trained on massive text datasets to predict next tokens and adaptable via fine-tuning for various business applications.
Highlights
1 clip · 1 min of 6 min-
- Transformers process sequential data like sentences and code efficiently.
- They evaluate each word in relation to all others to build comprehensive context.
That’s all the highlights.
Questions this video answers
8 · every answer cites the moment it was said01 What is a large language model? 2 sources · 0:36
A large language model is a specific instance of a foundation model applied to text and code. These models are pre-trained on massive unlabeled datasets to generate human-like text outputs.
02 How large are the datasets used to train LLMs? 2 sources · 1:27
LLMs are trained on enormous text datasets that can span up to petabytes of data. For example, GPT-3 was pre-trained on a corpus containing 45 terabytes of data.
03 What role do parameters play in model complexity? 2 sources · 2:04
Parameters are independent values that a model changes as it learns from data. A higher parameter count allows the model to handle greater complexity, such as GPT-3 using 175 billion parameters.
04 What are the three core components of an LLM? 2 sources · 2:30
An LLM is built upon three fundamental components: data, architecture, and training. Together, these elements enable the model to process information and generate coherent text.
05 How does the transformer architecture process text sequences? 2 sources · 2:55
The transformer is a neural network architecture designed to handle sequences like sentences or code lines. It understands context by analyzing each word in relation to every other word in a sentence.
06 How does training improve word prediction? 2 sources · 3:29
During training, the model learns to predict the next word in a sentence starting from random guesses. It iteratively adjusts its parameters to minimize the difference between its predictions and actual outcomes.
07 Why is fine-tuning used after general training? 2 sources · 4:02
Fine-tuning trains the model on a smaller, more specific dataset to refine its understanding. This process transforms a general language model into an expert at a designated task.
08 What are some practical business applications for LLMs? 2 sources · 4:27
Businesses leverage LLMs to build intelligent customer service chatbots that handle queries. They also utilize these models for content creation like writing scripts and assisting software developers with generating code.
Section-by-section summary
4 parts
Foundation models and large language models
Large language models are specific instances of foundation models pre-trained on vast amounts of unlabeled text and code. These models leverage massive parameter counts and petabyte-scale datasets to produce generalizable outputs.
The three core components of LLMs
LLMs rely on data, architecture, and training as their foundational components. The architecture uses neural networks, specifically transformers, to process sequential data and capture contextual relationships.
Next-token prediction and fine-tuning
During training, models iteratively learn to predict the next word in a sequence and adjust their internal parameters. Fine-tuning allows these general models to specialize in specific tasks using targeted datasets.
Practical business applications
Businesses apply LLMs across customer service chatbots, automated content creation, and software development assistance. As technology evolves, these models continue to expand into more innovative enterprise use cases.
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Foundation model1× · first 0:40
A model pre-trained on large amounts of unlabeled data to produce generalizable outputs.
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Large language model7× · first 0:03
An instance of a foundation model applied specifically to text and text-like data.
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Parameter2× · first 1:59
A value the model can change independently as it learns to increase complexity.
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Transformer2× · first 0:00
A neural network architecture that enables models to handle data sequences and understand context.
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Fine-tuning1× · first 4:02
The process of refining a general model on a specific dataset to master a task.
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Chatbot1× · first 4:27
An intelligent customer service application built using LLMs to handle queries.
731 words · about 3 min to read
GPT, or Generative Pre-trained Transformer, is a large language model, or an LLM, that can generate human-like text. And I've been using GPT in its various forms for years. In this video we are going to number 1, ask "what is an LLM?" Number 2, we are going to describe how they work.
Foundation models and large language models
And then number 3, we're going to ask, "what are the business applications of LLMs?" So let's start with number 1, "what is a large language model?" Well, a large language model is an instance of something else called a foundation model. Now foundation models are pre-trained on large amounts of unlabeled and self-supervised data, meaning the model learns from patterns in the data in a way that produces generalizable and adaptable output.
And large language models are instances of foundation models applied specifically to text and text-like things. I'm talking about things like code. Now, large language models are trained on large datasets of text, such as books, articles and conversations. And look, when we say "large", these models can be tens of gigabytes in size and trained on enormous amounts of text data.
We're talking potentially petabytes of data here. So to put that into perspective, a text file that is, let's say, one gigabyte in size, that can store about 178 million words. A lot of words just in one Gb. And how many gigabytes are in a petabyte? Well, it's about 1 million.
Yeah, that's truly a lot of text. And LLMs are also among the biggest models when it comes to parameter count. A parameter is a value the model can change independently as it learns, and the more parameters a model has, the more complex it can be. GPT-3, for example, is pre-trained on a corpus of actually 45 terabytes of data,
The three core components of LLMs
and it uses 175 billion ML parameters. All right, so how do they work? Well, we can think of it like this. LLM equals three things: data, architecture, and lastly, we can think of it as training.
1 Highlight · Transformer architecture and context handling
Those three things are really the components of an LLM. Now, we've already discussed the enormous amounts of text data that goes into these things. As for the architecture, this is a neural network and for GPT that is a transformer. And the transformer architecture enables the model to handle sequences of data like sentences or lines of code.
And transformers are designed to understand the context of each word in a sentence by considering it in relation to every other word. This allows the model to build a comprehensive understanding of the sentence structure and the meaning of the words within it. And then this architecture is trained on all of this large amount of data.
Next-token prediction and fine-tuning
Now, during training, the model learns to predict the next word in a sentence. So, "the sky is..." it starts off with a with a random guess, "the sky is bug". But with each iteration, the model adjusts its internal parameters to reduce the difference between its predictions and the actual outcomes. And the model keeps doing this gradually improving its word predictions until it can reliably generate coherent sentences.
Forget about "bug", it can figure out it's "blue". Now, the model can be fine tuned on a smaller, more specific dataset Here the model refines its understanding to be able to perform this specific task more accurately. Fine tuning is what allows a general language model
Practical business applications
to become an expert at a specific task. OK, so how does this all fit into number 3, business applications? Well, for customer service applications, businesses can use LLMs to create intelligent chatbots that can handle a variety of customer queries, freeing up human agents for more complex issues. Another good field, content creation. That can benefit from LLMs which can help generate articles, emails, social media posts, and even YouTube video scripts.
Hmm, there's an idea. Now, LLMs can even contribute to software development. And they can do that by helping to generate and review code. And look, that's just scratching the surface. As large language models continue to evolve, we're bound to discover more innovative applications. And that's why I'm so enamored with large language models.
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