Pre-Training Process
Overview
The pre-training phase is where the core foundation of a large language model (LLM) is laid. Imagine it as a massive learning experience where the model ingests and assimilates information from a vast corpus of data. Think of the internet as a library, and pre-training as reading every book in the library. It's not about specific skills, but rather about building a broad understanding of language and concepts.
Key Objectives
The primary objectives of pre-training are:
- Imitation: The model learns to mimic the patterns and structures found in the training data. It's about learning how language is used, what makes text "look" natural, and what kinds of concepts are often associated with each other.
- Maximize Likelihood: The model learns to predict the next token (word or part of a word) in a sequence, given the previous tokens. This process helps the model develop a strong sense of probability and fluency.
A Simplified Analogy
Imagine a language model as a student learning to speak English. During pre-training, the model is exposed to vast amounts of text: novels, articles, websites, and more. It learns to recognize patterns in language:
- Grammar: The model learns about the rules of grammar, how words are arranged in sentences, and the proper use of punctuation.
- Vocabulary: It expands its vocabulary by encountering new words and understanding their meanings in context.
- Concept Association: The model learns that certain words often appear together (like "cat" and "meow") and that these combinations can form meaningful concepts.
Training Data
The quality and diversity of training data are crucial to a model's success. The data used in pre-training typically comes from:
- Web Pages: Textual content scraped from websites, including articles, blog posts, forums, and social media.
- Books: Large collections of digitized books, including fiction, non-fiction, and educational materials.
- Code: Source code from various programming languages, which exposes the model to syntax and programming concepts.
The Role of Tokens
Tokens are the building blocks of text. Think of them as individual words or parts of words. Here's how they function in pre-training:
- Segmentation: Text is divided into individual tokens for processing.
- Probability: The model assigns a probability to each token based on its likelihood of appearing in a given context.
- Prediction: The model predicts the next token in a sequence based on the probabilities assigned to the previous tokens.
The Importance of Calibration
Because the model assigns probabilities to tokens, it becomes highly calibrated. This means:
- Diverse Personas: The model can effectively take on different "personas" or styles of writing based on the context and the desired output.
- Content Generation: It can generate various types of text, including news articles, poems, code, and even conversational dialogue.