Deep Dive into LLMs like ChatGPT

About this video

• Video Title: Deep Dive into LLMs like ChatGPT
• Channel: Andrej Karpathy
• Speakers: Andrej Karpathy
• Duration: 03:31:24

Overview

This video provides a comprehensive, yet accessible, introduction to large language models (LLMs) like ChatGPT. It covers the entire pipeline of how these models are built, from data collection and pre-training to fine-tuning and reinforcement learning, explaining the underlying concepts and practical implications for a general audience.

Key takeaways

• Pre-training Data: LLMs are trained on vast amounts of text data from the internet, which is then processed and filtered through various stages, including URL filtering, text extraction, and language filtering. The resulting dataset can be tens of terabytes in size and contain trillions of tokens.
• Tokenization: Text is converted into tokens, which are essentially chunks of text that the model processes. The Byte Pair Encoding (BPE) algorithm is used to create a vocabulary of around 100,000 tokens, which helps balance sequence length and symbol representation.
• Neural Network Internals: LLMs, typically based on the Transformer architecture, are complex mathematical functions with billions of parameters. They process token sequences and predict the next token in a sequence through a series of computations.
• Training and Inference: Training involves iteratively updating the model's parameters to improve its predictions based on the training data. Inference is the process of generating new text by sampling tokens from the model's probability distributions.
• Post-training Stages: After pre-training, models undergo supervised fine-tuning (SFT) on conversational data and reinforcement learning (RL) to align their behavior with desired characteristics like helpfulness, truthfulness, and harmlessness, and to improve reasoning capabilities.
• Emergent Behaviors: LLMs exhibit emergent behaviors like hallucinations (making up information), a limited understanding of their own identity, and difficulties with certain tasks like spelling or precise counting due to their token-based processing and finite computational capacity per token. Tools like web search or code interpreters can help mitigate these limitations.
• Reinforcement Learning from Human Feedback (RLHF): This technique trains a reward model to simulate human preferences, allowing for RL in unverifiable domains like creative writing. However, RLHF is susceptible to gaming the reward model with adversarial examples.
• Future Trends: LLMs are evolving towards multimodality (handling audio, images, and text), increased autonomy through agents, and more pervasive integration into various tools, while the role of human supervision remains crucial.