You already know what Large Language Models (LLMs) are, but how do they “learn” to create text that seems almost human? The process of training an LLM is similar to how a person learns — it involves two main stages: massive reading and focused practice. Let’s break down how it works.
Stage 1: Pre-training
This is the most extensive and resource-intensive stage. The model is “fed” an enormous amount of text data collected from the internet, books, encyclopedias, and articles. The volume of this data is measured in terabytes.
The goal of this stage is to teach the model to predict the next word in a sentence based on the preceding ones. For example, if the model sees “The cat sat on the…”, it must learn to predict a word like “mat,” “roof,” or “porch” with high probability. This process helps the model grasp grammatical rules, language structure, and even some facts about the world. During this stage, key technologies like transformers and vector embeddings play a crucial role. They help the model understand not just each word individually, but the entire phrase’s context.
Stage 2: Fine-tuning
After pre-training, the model has broad but not very precise knowledge. To make it useful for specific tasks (like answering questions, writing code, or translating), it undergoes a fine-tuning process.
In this stage, the model is trained on much smaller, but higher-quality and more specialized datasets. A human operator might ask questions and show the model what the correct answer should be. This process helps the model learn to follow instructions and adapt its style for particular tasks.
Key Challenges: Hallucinations and Bias
Even the most advanced LLMs aren’t perfect. During the training process, they can face two serious problems:
- Hallucinations: This is when a model generates information that is factually incorrect but sounds very convincing. It doesn’t have access to real-world data and “invents” the answer based on statistical patterns.
- Bias: A model can inherit biases from the data it was trained on. If the data contains stereotypes, the model will likely reproduce them.
Developers are constantly working to solve these issues by implementing stricter data controls and special algorithms to make the models more reliable and ethical.
Training an LLM is a complex, multi-stage process that requires immense computational resources. Understanding these stages will help you better appreciate the capabilities and limitations of these powerful tools.
