What is a Large Language Model?

As we set sail on this educational journey, it's essential to begin with a critical question—What are Large Language Models (LLMs)?

What This Video Will Teach You:

• A straightforward explanation of Large Language Models and their essence.
• The wide array of applications enabled by LLMs.

Here you'll learn the fundamental groundwork, ensuring a seamless kickoff to your exploration.

After absorbing the insights from the introductory video on Large Language Models (LLMs), you've taken the first step in understanding these advanced AI tools. Let's delve a bit further, clarifying the concept of language models in an easily digestible manner and smoothly transitioning into how these models work.

Understanding Language Models

Imagine a language model as a brilliant system that can predict the next word in a sentence. It's akin to a friend who's good at guessing the end of your sentences, but this friend has read almost every book available and remembers them all. It uses this vast knowledge to make educated guesses about the next word based on the already-used words.

• Example: For simple words like "bird," "flies," "in," "the," and "sky," the language model can quickly tell that "The bird flies in the sky" is a logical sequence, whereas "Sky the bird flies in" doesn't make much sense.

These models aren't just about guessing words; they're about understanding the flow and rules of language, almost as if they've absorbed some of the essence of how we communicate and share ideas.

Modern LLMs, especially those built on the Transformer architecture (as we'll cover after the modules around vector embeddings), leverage deep learning techniques to analyze and generate text. These advanced neural networks are adept at understanding context and generating coherent, contextually appropriate text. Think of these models as having an intricate web of neurons that mimic human brain activity, allowing them to grasp the subtleties of language and produce responses that feel surprisingly human-like. This neural network framework is the backbone of their ability to comprehend and generate language, providing them with the flexibility to apply this understanding across various tasks, from text generation to translation and more.

A Brief History of Time LLMs

The journey of language models began with Claude Shannon's curiosity about communication and information theory in 1948, leading to practical applications in speech recognition and machine translation around the 1980s. Initially, these tools were simple, using basic counting methods to guess the next word in a sentence.

The advent of neural networks with Bengio et al. 2003 marked a significant leap forward. Think of neural networks as a way for computers to start thinking more about the words and sentences they encounter, somewhat like how we understand conversations by considering their context. These allowed models to consider longer text sequences and understand more complex patterns.

Then came the recurrent neural networks (RNNs) and long short-term memory (LSTMs) networks. These allowed computers to remember what they read a few sentences ago, helping them use past information to make better predictions about what comes next. This was a significant step forward because, previously, models could only look at a few words at a time without "remembering" the earlier parts of the sentence.

In 2017, the famous Transformers Architecture was published in the paper "Attention is All You Need" by Vaswani et al. 2017. It led to the development of BERT and GPT, and the transformers architecture is the backbone of the most popular LLMs so far, be it ChatGPT, Bard, Claude, Mistral-7b, or Llama-2. Transformers improved upon RNNs and LSTMs by being better at handling sequences of words. Instead of reading a text one word after another, Transformers can pay attention to multiple words at once, which helps them understand the context more effectively. And while so far we've covered.

This history doesn't cover all the significant events, and it wouldn't be incorrect to call this an oversimplification. But the essence of this context was to make you imagine how so many things went right for us to read this coursework today.

And the exciting part? We're still just scratching the surface. There's so much more to explore and discover in this field. Perhaps you might even join in and contribute to the next big discovery!