2 min read

Andrej Karpathy Explains Large Language Models (LLM’s)

Over the Thanksgiving break, a significant event occurred in the AI community: Andrej Karpathy, a renowned figure known for his pivotal role in developing Tesla's Autopilot system and his contributions as a founding member to OpenAI, delivered a comprehensive overview of Large Language Models (LLMs).

Core Takeaways

Karpathy's talk provided several key insights into the nature, capabilities, and challenges of LLMs:

  • Fundamentals of LLMs: LLMs are essentially neural networks trained on vast text data to predict subsequent words. They consist of a parameters file and a run file, signifying their operational simplicity yet complex underlying structure.
  • Training Process: They undergo two critical training phases – pre-training on raw internet text and fine-tuning on human-labeled datasets. This dual approach helps build a general knowledge base and shape them into useful assistants.
  • Empirical Black Boxes: Despite understanding their mathematical functions, the specific roles of individual parameters in LLMs remain largely unknown. Their efficacy is judged based on behavioral outputs rather than structural understanding.
  • Evolving Capabilities: LLMs are rapidly advancing, incorporating abilities like tool use, multimodality, and customization, transforming them into central components of broader computational systems.
  • Security Concerns: The talk also highlighted the risks involved, such as model hijacking and data poisoning. Ongoing efforts in defense mechanisms are crucial to maintaining their integrity.
  • Cognitive Modes: Currently, LLMs operate on a 'system 1' mode of thinking – quick, instinctive responses. The transition to a 'system 2' mode, which involves more reflective and deliberate processing, is a critical area of research.
  • Self-Improvement and Scaling Laws: Drawing inspiration from systems like AlphaGo, Karpathy addressed the challenges in implementing self-improvement mechanisms in LLMs due to the absence of clear reward signals in language tasks. Additionally, he discussed the scaling laws, indicating improvements with increased data and parameters, albeit with rising computational costs.
  • Customization and Evolutionary Parallels: The future might see more specialized LLMs, akin to an app store for GPT-3, catering to specific needs. Karpathy also drew parallels between the evolution of LLMs and early operating systems, highlighting the emergence of open-source ecosystems and the reuse of memory and processing concepts.


Karpathy’s unique perspective, grounded in both academic research and practical application, offers a comprehensive understanding of where these AI technologies are heading. His insights will undoubtedly serve as a valuable guide to anyone navigating this ever-evolving landscape as we continue to explore the possibilities of LLMs and AI.

Nevertheless, if you’re getting into Generative AI and wondering what the hype is about, finding a better fundamental resource would be pretty challenging.