How Are Large Language Models Built and Trained? | Stanford CS229 Machine Learning

TL;DR

Large language models are built through pretraining and post-training, with practical success depending heavily on data, evaluation, and systems. The Stanford CS229 lecture identifies five training components: architecture, training loss and algorithm, data, evaluation, and systems, while explaining how autoregressive models predict each next token from its context. Read on to understand the complete training pipeline and why its less-discussed components matter.

Transcript

So, let's get started. So I'll be talking about building LLMs today. So I think a lot of you have heard of LLMs before, but just as a quick recap. LLMs standing for large language models are basically all the chat bots that you've been hearing about recently. So, ChatGPT, from OpenAI, Claude, from Anthropic, Gemini and Llama, and other types of mod... Read More

Key Insights

• LLMs are neural networks that learn language patterns by analyzing vast datasets from the internet.
• Pretraining involves modeling probability distributions over sequences of tokens to predict the next word.
• Tokenization is crucial for converting text into a format LLMs can process, typically using methods like Byte Pair Encoding.
• Perplexity measures a model's ability to predict a sample and has improved significantly from 2017 to 2023.
• Post-training aligns LLMs with human preferences using supervised fine-tuning and reinforcement learning.
• Reinforcement learning from human feedback (RLHF) uses human-labeled preferences to improve model outputs.
• Scaling laws predict model performance improvements with increased data and larger models, without overfitting.
• Systems optimization, including GPU utilization and low precision computation, is vital for efficient LLM training.

Questions & Answers

Q: What does the Stanford CS229 lecture explain about building LLMs?

The lecture gives an overview of the components needed to train large language models such as ChatGPT, Claude, Gemini, and Llama. It covers pretraining and post-training, concentrating on training methods, data, evaluation, and systems rather than repeating an earlier lecture about transformer architecture.

Q: What are the five key components of training large language models?

The five components are architecture, training loss and algorithm, data, evaluation, and systems. The lecture emphasizes that data, evaluation, and systems matter most in practice and receive much of the industry’s attention.

Q: What is the difference between LLM pretraining and post-training?

Pretraining uses the classical language-modeling paradigm to train a model to represent language across internet data. Post-training takes a large language model and makes it function more like an AI assistant; the lecture associates GPT-2 and GPT-3 with pretraining and ChatGPT with post-training.

Q: How do large language models learn language patterns during pretraining?

A language model learns a probability distribution over sequences of tokens or words. During autoregressive modeling, it predicts each next word from everything that appeared earlier in the sequence, which is called the context.

Q: How does an autoregressive language model work?

It decomposes the probability of a sequence into successive conditional probabilities: the first word, the second given the first, and each later word given all previous words. This decomposition follows the chain rule of probability and does not require an approximation.

Q: Why are language models called generative models?

A generative model can produce sentences or other data. Once a language model represents a probability distribution over sequences, sentences can be generated by sampling from that distribution.

Q: What kinds of knowledge can a language model capture?

A language model can assign different probabilities based on syntactic and semantic knowledge. The lecture contrasts “the mouse ate the cheese” with a grammatically flawed sentence and with “the cheese ate the mouse,” which is less plausible semantically.

Q: Why are data, evaluation, and systems important when training LLMs?

Data determines what the model trains on, while evaluation shows whether training is making progress toward the intended goal. Systems determine how these very large models run on modern hardware, making systems work especially important for LLM training.

Summary & Key Takeaways

• Large language models are trained in two main phases: pretraining and post-training. Pretraining involves exposing the model to a vast corpus of text to learn language patterns, while post-training aligns the model's outputs with human preferences using techniques like supervised fine-tuning and reinforcement learning from human feedback.

• Tokenization is a key step in preparing data for LLMs, converting text into tokens that the model can process. Perplexity is used to evaluate LLM performance, reflecting the model's ability to predict the next word in a sequence. Evaluation methods have evolved to include human preference assessments.

• Systems optimization plays a critical role in LLM training, focusing on efficient GPU utilization and low precision computation to handle the massive data and computation requirements. Scaling laws guide resource allocation, predicting performance improvements with increased data and model size.