Large Language Models: How They Work
Large Language Models (LLMs) represent one of the most significant advancements in machine learning, built to process and generate human-like text. Their capabilities stem from years of research in artificial intelligence (AI), machine learning (ML), and deep learning. In this blog post, we will delve into the technical mechanisms that enable these models to function effectively.
Understanding the Basics: What is a Large Language Model?
LLMs are a type of neural network designed to work with text data. They are trained on massive datasets and contain billions (sometimes trillions) of parameters. The term “large” refers to the sheer size of these models, both in terms of data used and the number of parameters involved.
The core purpose of an LLM is to predict the next word in a sequence of text. For example, given the input "The cat is on the", the model predicts likely next words such as "mat" or "roof." By iterating this prediction step, the model can generate coherent and contextually relevant text.
The Foundations: Layers of AI
To understand how LLMs work, it helps to see where they fit within the broader field of AI:
Artificial Intelligence (AI): A broad term for machines that simulate aspects of human intelligence.
Machine Learning (ML): A subset of AI that focuses on identifying patterns in data and making predictions.
Deep Learning: A specialised area of ML that uses artificial neural networks to process unstructured data like text and images.
Large Language Models: A specific application of deep learning focused on processing and generating text.
Training an LLM: A Three-Stage Process
LLMs are typically trained in three main stages:
1. Pre-training
In this stage, the model learns to predict the next word in a sequence using vast amounts of text data. This process is called self-supervised learning because the training data is inherently labeled—the next word is the label for the preceding sequence. By ingesting billions of sentences, the model learns:
Grammar and syntax.
Relationships between words and their meanings.
Patterns in how text is structured across different contexts.
For example, when given a sequence like “A flock of”, the model learns that “geese” or “sheep” might follow, based on patterns in the data.
2. Fine-tuning
After pre-training, the model undergoes fine-tuning to make it more useful for specific tasks. For instance, instead of predicting any next word, the model learns to answer questions, summarise text, or translate languages. This involves training on carefully curated datasets where human-provided inputs and outputs serve as examples.
3. Reinforcement Learning from Human Feedback (RLHF)
Some LLMs are further optimised using RLHF. Here, human reviewers rank the quality of the model’s outputs. The model uses these rankings to refine its responses, aligning better with human preferences. This phase is crucial for making models like ChatGPT more interactive and context-aware.
The Neural Network Architecture: Transformers
At the heart of an LLM lies the transformer architecture. Transformers are neural networks specifically designed to process sequential data efficiently. Unlike earlier models, which struggled to handle long dependencies in text, transformers use a mechanism called attention to focus on the most relevant parts of the input sequence.
Key Components of Transformers:
Embeddings: Words are converted into numeric vectors (word embeddings) that capture their semantic and syntactic properties.
Attention Mechanism: The model assigns different levels of importance to each word in the input, enabling it to understand context effectively.
Feedforward Layers: These layers process the attention outputs to make predictions about the next word.
For example, in the sentence “The dog barked because it was startled,” the attention mechanism helps the model associate “it” with “dog” rather than another part of the sentence.
Generating Text: A Step-by-Step Process
Once trained, an LLM generates text by predicting one word at a time:
Input Sequence: The user provides an initial sequence of words.
Prediction: The model predicts the most likely next word, or samples from the top few probabilities for more variation.
Iteration: The predicted word is added to the sequence, and the process repeats until a complete output is generated.
For example, starting with “Once upon a time”, the model might generate: “there was a brave knight who sought adventure.”
Limitations and Challenges
While LLMs are highly capable, they have limitations:
Hallucinations: They can generate incorrect or nonsensical information because they are trained to produce plausible-sounding text, not factual accuracy.
Context Length: Models have a limited memory of earlier parts of the input, which can lead to coherence issues in longer outputs.
Data Bias: The models can inadvertently replicate biases present in their training data.
Large Language Models are a fascinating blend of machine learning and linguistic processing. By leveraging vast datasets, intricate neural network architectures, and sophisticated training techniques, they can perform an array of text-based tasks. While they are not without their challenges, understanding the technical underpinnings of LLMs provides valuable insights into their capabilities and limitations.
