How do LLMs work?

Large language models (LLMs) like GPT-4 operate by processing sequences of text to predict the next word or token in a sequence. At their core, they use a neural network architecture called the transformer, which relies on self-attention mechanisms to analyze relationships between words in a sentence. Unlike earlier models that processed text sequentially (e.g., RNNs), transformers evaluate all words in a sentence simultaneously, allowing them to capture context more efficiently. For example, when processing the sentence “The cat sat on the mat,” the model assigns weights to each word to determine how much attention “cat” should pay to “mat” versus other words. This parallel processing enables LLMs to handle long-range dependencies and complex sentence structures effectively.

Training LLMs involves two main phases: pre-training and fine-tuning. During pre-training, models ingest massive datasets—often terabytes of text from books, websites, and other sources—to learn statistical patterns. They use a self-supervised objective, such as predicting masked words (e.g., filling in “The [MASK] sat on the mat”) or generating the next word in a sequence. For instance, given the input “The capital of France is,” the model learns to output “Paris” by adjusting its internal parameters through backpropagation. This phase requires significant computational power, often involving thousands of GPUs or TPUs. After pre-training, models are fine-tuned on smaller, task-specific datasets (e.g., question-answering pairs) to adapt them for applications like chatbots or code generation.

During inference, LLMs generate text by iteratively predicting the most probable next token based on the input and previously generated tokens. For example, if a user asks, “How do I sort a list in Python?” the model might output code snippets using its understanding of Python syntax and common sorting algorithms. To balance creativity and accuracy, techniques like temperature scaling adjust the randomness of predictions—higher temperatures increase diversity, while lower values produce more deterministic outputs. However, LLMs have limitations: they can hallucinate incorrect information, struggle with real-time data, and require substantial hardware for deployment. Developers often mitigate these issues by combining LLMs with external databases or validation systems to enhance reliability.