An introduction to Large Language Models
Last updated
Last updated
The rise of Large Language Models (LLMs) has undeniably transformed the way we interact with technology, bringing artificial intelligence into everyday conversations and making it more accessible than ever before. What once seemed like science fiction is now a natural part of our routines; with machines capable of writing our emails, answering our questions, and performing tasks that were formerly reserved for humans. But this transformation did not happen overnight. The shift from simple rule-based systems to modern Large Language Models (LLMs) is the result of years of progress in the field of natural language processing (NLP).
To fully understand LLMs, we need to trace the evolution of the underlying technologies, starting with neural networks and moving on to the important breakthroughs that led to the transformer-based models we use today.
Neural networks are computational models that mimic the architecture of the human brain. These networks are composed of layers of artificial neurons that process incoming input via a series of mathematical operations. A neural network can learn to recognize patterns in data by changing the weights of neurons connections during training, allowing the model to make predictions or decisions based on new data.
In the early days of artificial intelligence, neural networks were rather simplistic and incapable of effectively handling the complexity of sequential data such as language. While they were capable of doing image recognition and categorization tasks, they struggled with activities requiring a more in-depth comprehension of context, such as language translation, question responding, or text generation.
Recurrent neural networks (RNNs) were created to address the issue of sequential dependencies in language. Unlike traditional Feedforward Neural Networks (FNNs), where information flows in one direction, RNNs have feedback loops that allow information to persist across time steps, enabling them to "remember" previous inputs and consider them when processing subsequent ones.
Despite being popular and effective for handling sequential data, RNNs still faced two major challenges:
Vanishing and Exploding Gradients: When training an RNN, the gradients—used to adjust the model’s weights—either became too small (vanishing gradients) or too large (exploding gradients), especially when sequences are long. This occurs because gradients are multiplied by values of the weight matrices multiple times, leading to exponential decay or growth.
Short-Term Memory: Even though RNNs have feedback loops designed to remember information, they still struggled with long-term dependencies; they had a hard time holding on to information over long sequences, like an entire paragraph or a full document, which limited their effectiveness in more complex tasks.
These particular issues prompted the development of Long Short-Term Memory (LSTM) networks. Which are a particular type of RNN that uses memory cells and gates to selectively recall or forget information, which allows them to handle long-range dependencies better than traditional RNNs and reducing the risk of vanishing gradients.
The transformer architecture marked a turning point in how we processed language with artificial intelligence. As opposed to previous models, Transformers took a completely different approach at handling sequences, eliminating the need for repetition and step-by-step processing. Instead they processed sequences using a mechanism known as self-attention, which accelerated and improved the process of understanding distant links between words.
The key innovation of the transformer architecture is the self-attention mechanism, which enables the model to understand the relationships between different words in a sentence, no matter how far apart they are.
Words as Vectors: Each word in a sequence is converted into a vector—a numerical representation of the word’s meaning. These word vectors are then transformed into three types of vectors: Query (Q), Key (K), and Value (V).
Calculating Similarity: To understand how words relate to each other, the model compares the Query (Q) of one word with the Key (K) of all other words in the sequence. This comparison produces a score that tells the model how much attention to give to each word based on its relevance to the current word.
Weighting Words: The scores (called attention scores) determine how much importance each word gets. In the following sentence "The cat sat on the mat," the word "cat" would pay more attention to "sat" because they are closely related in meaning. The attention score between "cat" and "sat" would be higher than between "cat" and "on."
Final Word Representation: After calculating the attention scores, the model applies these to weight the Value (V) vectors. This means that words that are essential in the context of others will have a greater impact on the final representation of the term. For example, "cat" would consider the weighted value of "sat" more than "on."
Multi-head attention improves upon this mechanism by running multiple attention processes in parallel with varying weights. That way each "head" of attention collects different aspects of word relationships; one attention head might focus on syntactic structures, while another may capture semantic relationships. By combining the outputs of these heads, the transformer is able to gather richer and more nuanced representations of the sequence.
Transformers process entire sequences of words all at once. This parallel processing is problematic because the order of the words is crucial for providing meaning in language. To deal with this, transformers use a mechanism called positional encoding. The idea is to add some extra information to each word that tells the model where in the sequence it lies. The positional encodings are often computed from sinusoidal functions, which ensures that every word gets a unique representation based on its position.
After the self-attention mechanism works its magic, transformers use a feedforward neural network to further enrich the representations of each word. These neural networks contain layers to add complexity and help the model understand how the words interrelate with each other. To improve the model's learning efficiency, layer normalization is also used, which stabilizes the training process and improves general performance.
