Welcome to the first part of several articles outlining the basics of Large Language Models. For some context, I am a software engineer, and not a data scientists, so it may have a more suitable bearing towards those implementing LLMs (training, deployment), but who knows. If you are a data scientist, or at least a budding one, and this is useful, let me know.
Today we will started with the Transformer Model, which is largely a technology that super accelerated LLM’s as we know them today.
In the world of Natural Language Processing (NLP) and machine learning, the Transformer model stands out as quite a revolutionary architecture.
The architecture was introduced in paper titled “Attention is All You Need” by eight scientists working at Google, back in 2017. The title, if not already obvious, was a word play in reference to the famous Beatles song ‘Love is all you need’.
Since then the Transformer model has become the backbone of most modern language models, including GPT, BERT, and many others.
Understanding what a Transformer is and why it’s so effective is helpful to understanding the internals of today’s modern large language models.
Why Transformers?
Before Transformers, NLP models primarily relied on Recurrent Neural Networks (RNNs) and their variants, such as Long Short-Term Memory (LSTM) networks. These models process sequences of data one step at a time, which leads to problems with parallelisation and limits their ability to capture long-range dependencies. RNNs, for example, would struggle with the vanishing gradient problem, making it difficult for them to remember important information in long sentences. While LSTMs improved on these issues, they still struggled with efficiency.
The Transformer model broke away from the sequence-processing paradigm by introducing a fully attention-based architecture. The introduction of the self-attention mechanism allows Transformers to process entire sequences in parallel, rather than step-by-step, and to weigh the importance of different words in a sequence regardless of their position. This innovation solved many of the limitations of RNNs, especially in handling long-range dependencies, leading to superior performance across a range of NLP tasks.
The Core Components of a Transformer Model
To understand the power of the Transformer, let’s break down its core components:
The Attention Mechanism
At the heart of the Transformer architecture is the attention mechanism, specifically self-attention. Attention allows the model to selectively focus on different parts of the input when making predictions, giving it the ability to weigh the importance of words or tokens relative to others in a sentence.
For example, in the sentence, “The dog sat on the mat,” the word “dog” is more related to “sat” than to “mat,” but all of these words could be important in understanding the sentence. The attention mechanism enables the model to understand and assign varying degrees of importance to each word in relation to others, no matter where they appear in the sequence.
Self-attention works by taking each word and computing its relationships (or “attention scores”) with every other word in the sequence. This results in a matrix of attention scores, where each word’s importance relative to others is captured, allowing the model to better process and understand complex language structures.
Positional Encoding
Since Transformers do not process sequences in a linear fashion like RNNs, they lose the inherent understanding of word order. For example, in a sentence, word order is crucial to meaning — “the dog sat on the mat” is far more logical to a human than “the mat sat on the dog.” To overcome this, Transformers use positional encoding to inject information about word positions into the model.
Positional encoding adds unique positional values to the input embeddings, allowing the model to differentiate between words based on their position in the sequence. This ensures that the model still captures the order of words and their relationships.
Multi-Head Attention
One of the key innovations in the Transformer model is multi-head attention. While the self-attention mechanism enables the model to focus on different parts of a sentence, multi-head attention takes this a step further by allowing the model to apply several attention mechanisms (or “heads”) simultaneously. Each head can focus on different aspects of the sentence.
For example, one head might focus on subject-verb relationships, while another might focus on noun-adjective pairs. This multi-headed approach allows the model to capture a richer set of dependencies and relationships between words, making the representation of language more nuanced and powerful.
Feedforward Neural Networks
After applying the multi-head attention, the Transformer passes the outputs through a feedforward neural network. This network, which is applied independently to each position in the sequence, consists of two layers with a ReLU activation function in between. These feedforward layers introduce non-linearity, allowing the model to learn complex relationships in the data.
Layer Normalization and Residual Connections
Transformers also employ layer normalization and residual connections to stabilize training and improve performance. Layer normalization ensures that each layer of the model maintains a stable distribution of activations, while residual connections help prevent the vanishing gradient problem by adding the input of a layer to its output. These architectural improvements ensure that the model trains efficiently and effectively, even when scaling to massive sizes.
Encoder and Decoder Architecture
The original Transformer model was designed with two key components: an encoder and a decoder.
The encoder processes the input sequence, such as a sentence, by passing it through several layers of attention and feedforward neural networks. The encoder outputs a set of vectors, each representing a token in the input sequence and its context.
The decoder takes the encoder’s output and generates a target sequence, such as a translation of the input sentence or a prediction of the next word in the sequence. The decoder also uses self-attention but incorporates the encoder’s output to guide the generation process. The decoder is particularly useful in tasks like machine translation or text generation.
While both components are essential for tasks like translation, some models (such as BERT) only use the encoder, while others (like GPT) focus solely on the decoder.
Advantages of the Transformer Model
Let’s round up some of the key advantages to the
Parallelization
Unlike RNNs and LSTMs, Transformers process all tokens in a sequence simultaneously rather than sequentially. This parallelization allows for much faster training, especially on large datasets, which is a critical factor behind the success of models like GPT and BERT.
2. Capturing Long-Range Dependencies
Traditional sequence models struggle with capturing dependencies between words that are far apart in a sentence. Because self-attention allows each word to attend to every other word in the sentence, Transformers excel at capturing long-range dependencies, enabling them to understand more complex relationships in language.
3. Scalability
Transformers are highly scalable, meaning that they can handle very large datasets and can be trained on vast amounts of text. This scalability has made it possible to train models with billions of parameters (like GPT-3), which are capable of achieving human-like understanding and generation of text.
4. Versatility
The Transformer architecture has been adapted for various tasks, not just in NLP but also in fields like computer vision (Vision Transformers or ViT) and even reinforcement learning. The core mechanism of self-attention is flexible and powerful, allowing it to be applied across different domains.
Applications of Transformers
There are a good number of models now, built upon the Transformers architecture, to name just a few classics:
GPT (Generative Pre-trained Transformer): A series of models designed for text generation, including chatbots and creative writing.
BERT (Bidirectional Encoder Representations from Transformers): Focuses on understanding the context of words from both directions in a sentence, excelling at tasks like question answering and sentiment analysis.
T5 (Text-to-Text Transfer Transformer): Converts all NLP tasks into text-to-text format, simplifying the process of fine-tuning for various tasks.
CodeLlama: A version of the Transformer optimised for code generation and understanding, used in AI-assisted programming CodeGeneration Tools
That’s it for this week, next time we will delve into Tokenizers!
