Deep Dive into LLMs like ChatGPT by Andrej Karpathy

General Information

• Title: Deep dive into LLMs like ChatGPT
• Presenter: Andrej Karpathy
• Medium: Youtube
• Date of Presentation: Feb 6, 2025
• Link: YouTube
• Date of Discussion: 2025.03.04

Summary

• Topic: A comprehensive, general-audience introduction to Large Language Models (LLMs) like ChatGPT, covering the entire pipeline from data collection to deployment.
• Key Concepts Covered:
  • Pre-training: Downloading and processing the internet (Common Crawl, filtering, etc.), tokenization (byte-pair encoding), neural network architecture (Transformers), training process (next token prediction, loss function), and inference (sampling from the model).
  • Post-training: Supervised Fine-Tuning (SFT) using conversations (human labelers, labeling instructions, synthetic data), addressing hallucinations (knowledge cut-off, tool use), and understanding LLM "psychology" (limitations in counting, spelling, etc.).
  • Reinforcement Learning (RL): Motivating RL through the analogy of school (textbooks, worked examples, practice problems), explaining RL in verifiable domains (math, code), introducing Reinforcement Learning from Human Feedback (RLHF) for unverifiable domains (creative writing), and discussing the limitations of RLHF (adversarial examples).
  • Future Directions: Multimodality, agents, pervasive/invisible AI, test-time training.
• Approach: Karpathy uses a step-by-step explanation, starting with the basics and gradually building up to more complex concepts. He uses analogies (e.g., school, DJ set, Swiss cheese), visualizations (neural network diagrams, token sequences), and real-world examples (ChatGPT, Llama 3, DeepSeek-R1) to make the concepts accessible.

Discussion Points

• Strengths:

  • Clarity and Accessibility: The video is exceptionally clear and well-structured, making complex topics understandable for a general audience. Karpathy's use of analogies and visualizations is particularly effective.
  • Comprehensive Coverage: The video covers the entire LLM pipeline, providing a holistic understanding of how these models are built and trained.
  • Practical Insights: The discussion of LLM limitations (hallucinations, counting, spelling) and the emphasis on using LLMs as tools are valuable for practical application.
  • Emphasis on the "Why": Karpathy doesn't just explain what happens, but also why things are done a certain way (e.g., why tokenization is used, why RL is powerful).
  • Discussion of RL and Thinking Models: The explanation of how RL leads to emergent "thinking" strategies in models like DeepSeek-R1 is a highlight.

• Weaknesses/Areas for Further Discussion:

  • Depth in Certain Areas: While comprehensive, some areas are necessarily glossed over (e.g., the mathematical details of Transformers, the specifics of RL algorithms).
  • Focus on Text: The video primarily focuses on text-based LLMs, with only a brief mention of multimodality.
  • Evolving Landscape: The field of LLMs is rapidly evolving, so some specific details (e.g., model names, leaderboard rankings) may become outdated quickly.
  • RLHF Limitations: The discussion of RLHF limitations is good, but could be expanded upon.

• Key Questions (from the discussion):

  • How can hallucinations be addressed at the architectural level, potentially using techniques like beam search or analyzing logit distributions? (This was a major point of discussion)
  • How do reasoning models handle tasks like counting, given their token-based nature? Is there an internal representation of characters, or is it purely associative?
  • To what extent do thinking strategies developed in verifiable domains (math, code) transfer to unverifiable domains (creative writing)?
  • How can we create better evaluation metrics and datasets for training and assessing LLMs, especially in the context of RL?
  • How can we overcome the limitations of RLHF, particularly the susceptibility to adversarial examples?

• Applications: The video highlights the broad applicability of LLMs, from question answering and text generation to code generation and problem-solving. The discussion of agents suggests future applications in automating complex tasks.

• Connections:

  • Other LLM Papers: The video references several key papers, including the InstructGPT paper, the Llama 3 paper, and the DeepSeek-R1 paper.
  • AlphaGo: The analogy to AlphaGo and move 37 highlights the potential of RL to discover novel strategies.
  • Distillation: The discussion mentions model distillation as a way to create smaller, more efficient models.
  • Tokenization: The discussion of tokenization limitations connects to research on character-level or byte-level models.

Notes and Reflections

• Interesting Insights:

  • The idea of LLMs as "internet simulators" and "lossy compressions of the internet" is a powerful and intuitive way to understand their capabilities and limitations.
  • The concept of LLM "psychology" and the "Swiss cheese" model of capabilities is helpful for understanding their unpredictable behavior.
  • The emergence of "thinking" in RL-trained models is a fascinating and potentially transformative development.
  • The limitations of RLHF highlight the ongoing challenges in training LLMs, particularly in subjective domains.

• Lessons Learned:

  • LLMs are powerful tools, but they are not infallible. It's crucial to understand their limitations and use them appropriately.
  • The training process for LLMs is complex and involves multiple stages, each with its own challenges and considerations.
  • Reinforcement learning is a key area of research for improving LLM capabilities, particularly in reasoning and problem-solving.
  • The field of LLMs is rapidly evolving, and it's important to stay up-to-date with the latest developments.

• Future Directions (from the discussion):

  • Exploring architectural modifications to address hallucinations.
  • Investigating the transfer of reasoning skills between verifiable and unverifiable domains.
  • Developing better evaluation metrics and datasets for LLMs.
  • Researching alternatives to or improvements upon RLHF.
  • Further exploration of test-time training.
  • Consideration of the ethical implications of increasingly powerful LLMs.