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Lesson 2: Deep Dive into LLMs
Exploring the Mechanics and Applications of Large Language Models
Welcome to the LLM Hub!
In this lesson, we dive into the fascinating world of Large Language Models (LLMs) such as GPT-3, which are reshaping the landscape of conversational AI. You’ll learn not just what LLMs are, but how they operate behind the scenes to perform tasks that were once the domain of traditional NLP and NLU technologies.
We start with a primer on the basic principles of LLMs, followed by an exploration of advanced techniques such as prompt design, engineering, and the strategic use of knowledge bases. We’ll also cover the critical processes of fine-tuning and pre-training, which refine and enhance an LLM’s performance.
What You'll Learn:
The Core Functions of LLMs: How these models generate text and interpret language.
Advanced Implementation Strategies: Including prompt engineering and the use of RAG for dynamic information retrieval.
Practical Applications: How these technologies are applied in real-world scenarios to create more intelligent and responsive AI agents.
Get ready to unlock the potential of LLMs and elevate your skills in AI development!
Time: 12-20 Min
Table of Contents
What are LLMs & How do they Work?
What is an LLM?
When a new technology really wows and gets us excited it becomes a part of us. We make it ours and we anthropomorphize it. We project human like qualities on it and this can hold us back from really understanding what we are actually dealing it.
So let's consider a few questions. Mainly what is an LLM and what are its limitations?
Perhaps these questions and ideas will illuminate our understanding:
Are LLMs a program?
Are LLMs a knowledge base?
Do LLMs know anything?
If an LLM is a program how does it compute its 70-100 Billion parameters in only a few seconds?
If an LLM is a knowledge base, why does it need to predict?
How can an LLM Model with Billions of parameters that has been trained on pretty much the entire internet, fit on a 100GB drive?
What are some simple tasks that LLMs can't do?
How do LLMs work?
LLMs condense knowledge into patterns and this includes words, word order, and how they are related to each other. These are represented mathematically via tokens. When you ask an LLM a questions, your question is turned into tokens and based on the LLM can predict what token should come next.
ChatGPT does it reading in one direction and is great an output. BARD does this bi-directionally and can predict future and past tokens.
One way to think about that questions asks like a filter. The filter is essentially the tokenized expression of your questions. The filter is then used to predict what will come out. If it's a coffee filter, probably coffee.
What are the Limitations of LLMs?
Why do good models do bad things? The answer lies in the models and how they are built.
LLMs are Statistical Representations of Knowledge Bases. They have taken the world's information and knowledge and boiled it down to statistical principles.
These principles are like icons. Icons represent something much more than what they are. They are a low resolution images that represent a much bigger chunk of information. They give you a lot more information than meets the eye.
Additionally, LLMs were trained on biased data. We know this because the internet is full of biased data. For example, most the internet is in English and represents Western values, yet the global population doesn't speak mostly english or hold western values.
When we combine both low-resolution models and bias we are bound to have hallucinations and poor accuracy.
How does this work and when does it happen?
It happens when you ask a detailed question about something specific. In our example, if you ask detailed questions about the icon, the model might make up those details in a way that conforms to its biases.
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