Introduction to Large Language Models and the Transformer Architecture.pdf

4/19/23, 1:17 AM Introduction to Large Language Models and the Transformer Architecture | byPradeep Menon | Mar, 2023 | Medium
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Introduction toLarge Language Modelsandthe
Transformer Architecture
ChatGPT is making waves worldwide, attracting over 1 million users in
record time. As a CTO for startups, I discuss this revolutionary technology
daily due to the persistent buzz and hype surrounding it. The applications of
GPT are limitless, but only some take the time to understand how these
models work. This blog post aims to demystify OpenAI’s GPT (Generative
Pre-trained Transformer) language model.
GPT (Generative Pre-trained Transformer) is a type of language model that
has gained significant attention in recent years due to its ability to perform
various natural languages processing tasks, such as text generation,
summarization, and question-answering.
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This blog post will explore the fundamental concept of LLMs (Large
Language Models) and the transformer architecture, which is the building
block of all language models with transformers, including GPT. By the end
of this post, you will have a basic understanding of the building blocks of a
Large Language Model, such as GPT.
Let’s start by understanding what Large Language Models (LLMs) are.
Large Language Models (LLM)
Large Language Models (LLMs) are trained on massive amounts of text data.
As a result, they can generate coherent and fluent text. LLMs perform well
on various natural languages processing tasks, such as language translation,
text summarization, and conversational agents. LLMs perform so well
because they are pre-trained on a large corpus of text data and can be fine-
tuned for specific tasks. GPT is an example of a Large Language Model.
These models are called “large” because they have billions of parameters
that shape their responses. For instance, GPT-3, the largest version of GPT,
has 175 billion parameters and was trained on a massive corpus of text data.
The basic premise of a language model is its ability to predict the next word
or sub-word (called tokens) based on the text it has observed so far. To better

understand this, let’s look at an example.
The above example shows that the language model predicts one token at a
time by assigning probabilities to tokens based on its training. Typically, the
token with the highest probability is used as the next part of the input. This
process is repeated continuously until a special <stop> token is selected.
The deep learning architecture that has made this process more human-like
is the Transformer architecture. So let us now briefly understand the

Transformer architecture.
The Transformer Architecture: The Building Block
The transformer architecture is the fundamental building block of all
Language Models with Transformers (LLMs). The transformer architecture
was introduced in the paper “Attention is all you need,” published in
December 2017. The simplified version of the Transformer Architecture
looks like this:

There are seven important components in transformer architecture. Let’s go
through each of these components and understand what they do in a
simplified manner:

1. Inputs and Input Embeddings: The tokens entered by the user are
considered inputs for the machine learning models. However, models
only understand numbers, not text, so these inputs need to be converted
into a numerical format called “input embeddings.” Input embeddings
represent words as numbers, which machine learning models can then
process. These embeddings are like a dictionary that helps the model
understand the meaning of words by placing them in a mathematical
space where similar words are located near each other. During training,
the model learns how to create these embeddings so that similar vectors
represent words with similar meanings.
2. Positional Encoding: In natural language processing, the order of words
in a sentence is crucial for determining the sentence’s meaning.
However, traditional machine learning models, such as neural networks,
do not inherently understand the order of inputs. To address this
challenge, positional encoding can be used to encode the position of
each word in the input sequence as a set of numbers. These numbers
can be fed into the Transformer model, along with the input embeddings.
By incorporating positional encoding into the Transformer architecture,
GPT can more effectively understand the order of words in a sentence
and generate grammatically correct and semantically meaningful output.
3. Encoder: The encoder is part of the neural network that processes the
input text and generates a series of hidden states that capture the
meaning and context of the text. The encoder in GPT first tokenizes the

input text into a sequence of tokens, such as individual words or sub-
words. It then applies a series of self-attention layers; think of it as
voodoo magic to generate a series of hidden states that represent the
input text at different levels of abstraction. Multiple layers of the encoder
are used in the transformer.
4. Outputs (shifted right): During training, the decoder learns how to guess
the next word by looking at the words before it. To do this, we move the
output sequence over one spot to the right. That way, the decoder can
only use the previous words. With GPT, we train it on a ton of text data,
which helps it make sense when it writes. The biggest version, GPT-3,
has 175 billion parameters and was trained on a massive amount of text
data. Some text corpora we used to train GPT include the Common Crawl
web corpus, the BooksCorpus dataset, and the English Wikipedia. These
corpora have billions of words and sentences, so GPT has a lot of
language data to learn from.
5. Output Embeddings: Models can only understand numbers, not text, like
input embeddings. So the output must be changed to a numerical format,
known as “output embeddings.” Output embeddings are similar to input
embeddings and go through positional encoding, which helps the model
understand the order of words in a sentence. A loss function is used in
machine learning, which measures the difference between a model’s
predictions and the actual target values. The loss function is particularly
important for complex models like GPT language models. The loss
function adjusts some parts of the model to improve accuracy by

reducing the difference between predictions and targets. The adjustment
ultimately improves the model’s overall performance, which is great!
Output embeddings are used during both training and inference in GPT.
During training, they compute the loss function and update the model
parameters. During inference, they generate the output text by mapping
the model’s predicted probabilities of each token to the corresponding
token in the vocabulary.
6. Decoder: The positionally encoded input representation and the
positionally encoded output embeddings go through the decoder. The
decoder is part of the model that generates the output sequence based on
the encoded input sequence. During training, the decoder learns how to
guess the next word by looking at the words before it. The decoder in
GPT generates natural language text based on the input sequence and the
context learned by the encoder. Like an encoder, multiple layers of
decoders are used in the transformer.
7. Linear Layer and Softmax: After the decoder produces the output
embeddings, the linear layer maps them to a higher-dimensional space.
This step is necessary to transform the output embeddings into the
original input space. Then, we use the softmax function to generate a
probability distribution for each output token in the vocabulary, enabling
us to generate output tokens with probabilities.
The Concept of Attention Mechanism

Attention is all you need.
The transformer architecture beats out other ones like Recurrent Neural
networks (RNNs) or Long short-term memory (LSTMs) for natural language
processing. The reason for the superior performance is mainly because of
the “attention mechanism” concept that the transformer uses. The attention
mechanism lets the model focus on different parts of the input sequence
when making each output token.
The RNNs don’t bother with an attention mechanism. Instead, they just
plow through the input one word at a time. On the other hand,
Transformers can handle the whole input simultaneously. Handling the
entire input sequence, all at once, means Transformers do the job faster
and can handle more complicated connections between words in the
input sequence.
LSTMs use a hidden state to remember what happened in the past. Still,
they can struggle to learn when there are too many layers (a.k.a. the
vanishing gradient problem). Meanwhile, Transformers perform better
because they can look at all the input and output words simultaneously
and figure out how they’re related (thanks to their fancy attention

mechanism). Thanks to the attention mechanism, they’re really good at
understanding long-term connections between words.
Let’s summarize:
It lets the model selectively focus on different parts of the input sequence
instead of treating everything the same way.
It can capture relationships between inputs far away from each other in
the sequence, which is helpful for natural language tasks.
It needs fewer parameters to model long-term dependencies since it only
has to pay attention to the inputs that matter.
It’s really good at handling inputs of different lengths since it can adjust
its attention based on the sequence length.
Conclusion
This blog post provides a primer to Large Language Models (LLMs) and the
Transformer Architecture that powers LLMs like GPT. Large Language
Models (LLMs) have revolutionized natural language processing by
providing models that generate coherent and fluent text. They are pre-
trained on massive amounts of text data and can be fine-tuned for specific
tasks. The Transformer architecture is the fundamental building block of all

LLMs. It has made it possible for models like GPT to generate more accurate
and contextually relevant output. With the ability to perform various natural
language processing tasks, such as text generation, summarization, and
question-answering, LLMs like GPT are opening up new possibilities for
communication and human-machine interaction. In short, LLMs have
greatly improved natural language processing. They have the potential to
enhance human-machine interaction in exciting ways.
References
1. Attention is all you need
2. Introducing ChatGPT (openai.com)
3. Transformers, Explained: Understand the Model Behind GPT-3, BERT, and
T5 (daleonai.com)
4.What are Transformer Neural Networks? —YouTube
5. Stanford Webinar — GPT-3 & Beyond

Gpt 3 NLP OpenAI Machine Learning

Introduction to Large Language Models and the Transformer Architecture.pdf

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