In the fast-evolving realm of artificial intelligence, few innovations have captured the imagination of researchers and entrepreneurs like LLMs.
It was the advent of GPT — that made this technology popular with the masses What started with GPT1, and underwent evolutions as GPT2 and GPT 3 culminated in what we today know as Chat GPT.
A specialised application based on all these three models but focusing on conversational AI.
But what exactly is the GPT in ChatGPT?
At its essence, GPT ( Generative Pre Trained Transformer) is a type of machine learning model designed to understand and generate human-like text.
Imagine having a conversation with a robot that understands jokes, answers questions, and even crafts stories; that’s GPT in simple terms.
The “Generative” in its name hints at its ability to create or generate content. Whether it’s completing a sentence, writing an essay, or even penning poetry, GPT is trained to produce coherent and contextually relevant text. The “Pre-trained” part signifies its initial training phase on vast amounts of data, allowing it to acquire a broad understanding of language. This extensive knowledge is then fine-tuned for specific tasks, making it versatile and adaptable.
What truly sets GPT apart, however, is its underlying architecture: the Transformer. This allows GPT to pay “attention” to different parts of a sentence, understanding context and relationships between words, no matter how far apart they are.
To keep it simple, GPT is like a linguistic wizard, blending vast knowledge from its training with the magic of the Transformer architecture. The result? An AI model that not only comprehends the intricacies of human language but can also emulate it with astonishing proficiency.
But what exactly are Transformers?
How do they work, and why are they causing such a stir in the tech world?
The advent of the Transformer architecture has revolutionised the field of natural language processing (NLP). It has become the backbone of many state-of-the-art LLM models, including BERT, GPT, and T5. The Transformer’s success can be attributed to its ability to handle long-range dependencies in text, its scalability, and its parallel processing capabilities. In the context of large language models, the Transformer architecture has enabled models to understand and generate human-like text with unprecedented accuracy.
At its core, the Transformer architecture is designed to process sequences, be it in the form of text, speech, or even images. Unlike its predecessors, which relied heavily on recurrent or convolutional layers, the Transformer solely depends on attention mechanisms to draw global dependencies between input and output. This is achieved through two main components: the self-attention mechanism and the feed-forward neural networks.
But all this seems too complex to comprehend. What does attention mechanism even mean? Let’s try to simplify it using a simple example of Lego’s.
Imagine you have a big box of LEGO bricks. Each brick represents a word or a piece of information. Now, let’s say you want to build a story or understand a story using these bricks.
The Transformer is like a magical LEGO builder. Instead of building one brick at a time, it can look at all the bricks at once and figure out which ones fit best together.
Let’s break down how this magical builder works:
Attention Mechanism
- Imagine you’re reading a story about a prince and a dragon. Sometimes, you need to remember that the prince has a shiny sword when you read about the dragon later in the story. The Transformer has a special tool called “attention” that helps it remember important parts of the story, like the prince’s shiny sword, even if they’re far apart.
Positional Encoding
- Every story has a beginning, middle, and end. The Transformer needs to know the order of the story. So, it uses special stickers called “positional encodings” to remember the order of the words. This way, it knows that “The prince defeated the dragon” is not the same as “The dragon defeated the prince.”
Layers and Stacking
- Remember how we can stack LEGO bricks on top of each other to make tall towers? The Transformer does something similar. It has layers, like floors in a building. Each floor looks at the story and tries to understand it better. The more floors (or layers) it has, the better it understands the story.
Parallel Processing
- Imagine if you had many hands and could build multiple parts of a LEGO castle at the same time. That would be super fast, right? The Transformer can do that! It can look at many words at once, and combine them to form rational sentences in a fraction of a second.
So, in simple words, the Transformer is like a magical LEGO builder that can quickly build or understand stories by looking at all the pieces at once, remembering important parts, knowing the order of the story, and using many layers to understand it better.
Now that we have gone through a simpler allegory, let’s delve back into the technical side of things, for a bit. The self-attention mechanism in thee Transformer allows the model to weigh the relevance of different words in a sequence relative to a particular word. For instance, in the sentence “The cat, which was black, sat on the mat,” when processing the word “sat,” traditional models might lose the context of “cat” due to the intervening words. However, the self-attention mechanism can associate “cat” with “sat” by assigning a higher weight, ensuring the relationship between the two words is captured.
Each Transformer layer contains a feed-forward neural network that operates independently on each position. These networks are responsible for the complex transformations of the data, ensuring that the model can learn intricate patterns and relationships.
One challenge with the Transformer architecture is its lack of inherent understanding of the sequence’s order. Since it doesn’t use recurrent layers, it doesn’t have a built-in sense of position. To overcome this, positional encodings are added to the embeddings at the input layer. These encodings provide information about the position of a word within a sequence, ensuring that the model can consider word order when making predictions.
But the most important capability that the Transformer architecture has, is its scalability. As the demand for larger and more accurate models grows, the Transformer’s design allows for easy scaling. This is achieved by stacking multiple layers of the architecture on top of each other. Each layer captures different levels of abstraction, enabling the model to understand both the minute details and the broader context of a text.
Furthermore, while traditional recurrent models process sequences word by word, making them inherently sequential and challenging to parallelise, the Transformer processes all words in a sequence simultaneously, making it much faster and more efficient when trained on hardware like GPUs.
What is the role of Transformer’s in LLMs?
When it comes to large language models like GPT (Generative Pre-trained Transformer) or BERT (Bidirectional Encoder Representations from Transformers), the Transformer architecture plays a pivotal role. These models are trained on vast amounts of data and have billions, or even trillions, of parameters. The Transformer’s ability to capture long-range dependencies and its scalability make it ideal for such large-scale tasks.
For instance, GPT, which is designed for a range of tasks from translation to question-answering, utilises the Transformer’s decoder stack. In contrast, BERT, which excels in understanding the context of words by looking at their surrounding words, leverages the Transformer’s encoder stack.
Again, this seems too complex to comprehend. What do encoders and decoders even mean? Let’s use our earlier toy models to try and break this down a bit
Let’s imagine the Lego toy factory. This factory has two main sections: one where they listen and understand what toy you want to build using Lego blocks (let’s call this the “Encoder”), and another where they build and show you the specific lego blocks which can build that toy (let’s call this the “Decoder”).
The Encoder is like an attentive ear that listens very carefully to your toy example or description. It tries to understand every detail you mention. Once it understands, it creates a special blueprint or picture of what you said.
Once the Encoder has the blueprint, it’s passed to the Decoder. The Decoder is like a magical lego blocks builder. It looks at the blueprint and starts building the blocks as well as the toy itself. It then shows you the finished toy
So how GPT and BERT fit in amidst these encoders and decoders?
Well, imagine you start telling a story, but you stop halfway and say, “What happens next?” GPT mainly uses the Decoder. It takes your half-story and tries to continue and finish it for you.
With BERT, it’s like you’re playing a game of hide-and-seek with words. You tell a story but hide some words. BERT uses the Encoder to listen and understand the story, then tries to guess the hidden words.
So, in the magical Lego toy factory, the Encoder listens and understands, while the Decoder builds and shows. GPT is like the expert toy builder (Decoder) that finishes the build of the lego blocks and the toy, while BERT is like the attentive ear (Encoder) that’s great at guessing hidden parts of a story.
Challenges and future directions
While the Transformer architecture has been immensely successful, it’s not without its challenges. Training large Transformer-based models requires significant computational resources, making it inaccessible to many researchers and developers. There’s also the issue of model interpretability. With billions of parameters, understanding why a model made a particular prediction can be challenging.
However, researchers are continuously exploring ways to make the architecture more efficient, reduce its environmental impact, and improve its interpretability. Techniques like knowledge distillation, where a smaller model is trained to mimic a larger model’s behaviour, are being explored to make these models more accessible.
The Transformer architecture in large language models, has undeniably reshaped the landscape of Natural Language Processing (NLP). Its ability to capture long-range dependencies, combined with its scalability and parallel processing capabilities, has made it the go-to choice for large language models. As we continue to push the boundaries of what’s possible in NLP, the Transformer’s foundational principles will evolve and play a crucial role in guiding future innovations.
