The Most Basic Guide to Understanding Transformers - The Backbone of LLMs

The Transformer architecture has revolutionized the field of natural language processing (NLP) by enabling models to handle long sequences of text more effectively than traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). In this blog post, we will delve into the key components of the Transformer architecture: the Attention mechanism, and the Encoders and Decoders.

Before we jump into Transformers, let’s get ourselves familiarized with some important principles of text generation.??

Sequence Modeling

Sequence modelling is a type of machine learning task where the input data is a sequence of elements, and the goal is to predict the next element in the sequence or to generate a new sequence based on the input. This is crucial in various applications such as:

• Natural Language Processing (NLP): Tasks like language translation, text generation, and sentiment analysis.
• Speech Recognition: Converting spoken language into text.
• Time Series Prediction: Forecasting stock prices, weather conditions, etc.

Let’s look at some examples:

Language Translation: Given a sentence in English, the model predicts the corresponding sentence in French.

Input: "How are you?"

Output: "Comment ?a va?"

Text Generation: Given a starting phrase, the model generates a continuation of the text.

Input: "Once upon a time,"

Output: "there was a brave knight who fought dragons and saved kingdoms."

Stock Price Prediction: Given historical stock prices, the model predicts future prices.

Input: [100, 101, 102, 103, 104]

Output: [105, 106, 107]

Sequence models have garnered a lot of attention because most of the data in the current world is in the form of sequences – it can be a number sequence, an image pixel sequence, a video frame sequence, or an audio sequence. Over the last decade, we have stored vast amounts of unstructured sequence data. Sequence models can turn this data into valuable insights.

Recurrent Neural Networks (RNNs) in Text Generation

Recurrent Neural Networks (RNNs) are a class of neural networks designed to handle sequential data. They work by maintaining a hidden state that captures information about previous elements in the sequence. This makes them suitable for tasks like text generation, where the context of previous words is essential for generating the next word.

How RNNs Work:

Hidden State: Think of this as the network's memory. At each step, the RNN looks at the current piece of data (like a word in a sentence) and combines it with what it remembers from before. This memory helps the RNN understand the context and keep track of important information as it processes the sequence.

Output Generation: Using this memory, the RNN produces an output at each step. For example, when generating text, it uses the context from previous words to decide the next word.

Example:

Consider the task of generating text one character at a time. Given the input sequence "hel", the RNN predicts the next character "l".

• Input: "h" -> Hidden State: h1
• Input: "e" -> Hidden State: h2
• Input: "l" -> Hidden State: h3
• Output: "l"

Limitations of RNNs:

• Vanishing Gradient Problem: Difficulty in learning long-range dependencies or long contexts due to gradients which is important for learning becoming very small.
• Sequential Processing: Inability to parallelize computations, leading to longer training times.
• Fixed Context Window: Limited ability to capture dependencies that are far apart in the sequence.

Transformers

Transformers are a type of neural network architecture introduced in the paper "Attention is All You Need". They address the limitations of RNNs by using a mechanism called "Attention" to process the entire sequence at once, allowing for parallelization and better handling of long-range dependencies.

Transformers as Auto-Regressive Models

Transformers can be used as auto-regressive models, where the output at each step is fed back into the model to generate the next token. This is particularly useful in tasks like text generation, where the model generates one word at a time based on the previously generated words.

How it Works:

Masked Self-Attention: During training, the model uses masked self-attention to prevent it from seeing future tokens in the sequence.

Teacher Forcing: The model is trained using the actual previous tokens as input.

Inference: During inference, the model generates tokens one by one, using its own previous outputs as input.

Example:

Consider generating a sentence starting with "The cat".

Step 1: Input: "The cat" -> Output: "sat"

Step 2: Input: "The cat sat" -> Output: "on"

Step 3: Input: "The cat sat on" -> Output: "the"

Step 4: Input: "The cat sat on the" -> Output: "mat"

Key Features of Transformers:

Attention Mechanism: Allows the model to focus on different parts of the sequence simultaneously.

Parallelization: Enables faster training by processing the entire sequence at once.

Handling Long-Range Dependencies: Better captures relationships between distant elements in the sequence.

The Transformer architecture consists of an encoder and a decoder, each made up of multiple layers. Each layer has two main components:

Multi-Head Self-Attention Mechanism: Allows the model to focus on different parts of the sequence simultaneously.

Feed-Forward Neural Network: Applies non-linear transformations to the input.

Consider a translation task from English to French.

Encoder: Processes the English sentence "How are you?" and generates a context vector.

Decoder: Uses the context vector to generate the French sentence "Comment ?a va?"

Attention Mechanism in Transformers

The Attention mechanism is a fundamental component of the Transformer architecture. It allows the model to focus on different parts of the input sequence when generating each part of the output sequence. This capability is particularly important for handling long sequences of text, as it helps the model capture dependencies between distant words and phrases, which is a limitation in traditional RNNs and LSTMs.

How the Attention Mechanism Works

The Attention mechanism works by computing a set of attention weights that determine the importance of each word in the input sequence relative to the current word being processed. These weights are used to create a weighted sum of the input representations, which is then used to generate the output.

Scaled Dot-Product Attention: This is the core of the Attention mechanism. It involves three main components:

• Query (Q): Represents the current word being processed.
• Key (K): Represents all words in the input sequence.
• Value (V): Represents the actual values of the words in the input sequence.

Consider the sentence "The cat sat on the mat."

• Query: "cat"
• Keys: ["The", "cat", "sat", "on", "the", "mat"]
• Values: ["The", "cat", "sat", "on", "the", "mat"]

The attention weights are computed as the dot product of the Query and Key, scaled by the square root of the dimension of the Key, and then passed through a softmax function to obtain the final weights.

Multi-Head Attention: This extends the basic attention mechanism by using multiple sets of Queries, Keys, and Values, allowing the model to focus on different parts of the input sequence simultaneously. The outputs of each attention head are concatenated and linearly transformed to produce the final output.

Self-Attention: This is a special case of the Attention mechanism where the Query, Key, and Value all come from the same sequence. It allows the model to capture dependencies within the same sequence, which is essential for tasks like language modelling and translation.

Encoders and Decoders in Transformers

The Transformer architecture consists of an Encoder and a Decoder, each composed of multiple layers.

Encoder:

The Encoder processes the input sequence and generates a set of continuous representations.

Each layer in the Encoder consists of two main components:

Self-Attention Mechanism: This allows the Encoder to focus on different parts of the input sequence as discussed above.

Feed-Forward Neural Network: This processes the output of the self-attention mechanism.

The output of each layer is passed to the next layer, and the final output of the Encoder is a set of continuous representations of the input sequence.

Decoder:

The Decoder generates the output sequence one element at a time. Each layer in the Decoder consists of three main components:

Self-Attention Mechanism: This allows the Decoder to focus on different parts of the output sequence generated so far.

Encoder-Decoder Attention Mechanism: This allows the Decoder to focus on different parts of the input sequence.

Feed-Forward Neural Network: This processes the output of the attention mechanisms.

The output of each layer is passed to the next layer, and the final output of the Decoder is the generated sequence.

Encoders are needed to process the input sequence and generate a set of continuous representations that capture the meaning and context of the input. This is essential for tasks like translation, where the input sequence needs to be understood before generating the output sequence.

Decoders are needed to generate the output sequence based on the continuous representations generated by the Encoder. The Decoder uses the self-attention mechanism to focus on different parts of the output sequence generated so far and the encoder-decoder attention mechanism to focus on different parts of the input sequence.

Wrapping Up

The Transformer architecture, with its Attention mechanism and Encoder-Decoder structure, has significantly advanced the field of NLP. By allowing models to handle long sequences of text and capture dependencies between distant words, Transformers have enabled more accurate and efficient language models. Understanding these key components is essential for anyone looking to delve into the world of deep learning and NLP.