Lecture on Large Language Models

Introduction

• Overview of a recent 30-minute talk on large language models (LLMs).
• Discussion on the Llama 270b model by Meta AI.
• Comparison to models like GPT (OpenAI) with closed architecture.

What is a Large Language Model?

• Consists of two files: a parameters file (70 billion parameters, 140GB) and a run file (neural network architecture code).
• Can run on a MacBook, does not need internet connection.
• Parameters represent a "lossy compression" of the internet, storing vast amounts of data in a compact form.

Training and Inference

• Training: Involves compressing large amounts of internet text data using powerful GPU clusters.
  • Example: Llama 270b requires 6,000 GPUs, 12 days, $2 million.
• Inference: Running the model—cheap compared to training.
• The model predicts the next word in a sequence, learning a lot about the world.
• Models achieve a "Gestalt" understanding rather than memorization of data.

Neural Network Architecture

• Transformer neural network architecture is used.
• The model learns to predict the next word, enabling it to "dream" or generate text resembling internet documents.
• Models behave as empirical artifacts; difficult to understand fully but can be measured and evaluated.

Fine-Tuning and Improving Models

• Pre-training: Phase where large datasets are used to gather knowledge.
• Fine-tuning: Involves customizing models into assistant models through high-quality Q&A datasets.
  • Uses human labelers to create ideal response data.
• Stage Two: Further fine-tuning using comparison labels (Reinforcement Learning from Human Feedback).
• Open-source models like Llama allow easier fine-tuning by users.

Current State and Future Directions

• Closed models (e.g., GPT, Claude) lead in performance, open models (e.g., Llama) follow closely.
• Scaling Laws: Model performance improves predictably with increased parameters and data.
• Capabilities: Models now use tools (browsers, calculators) for enhanced problem-solving.
• Multi-modality: Involves understanding and generating from multiple types of input (text, images, audio).

System One vs. System Two Thinking

• Current language models operate on instinct (System One).
• System Two involves deeper reasoning and problem-solving, future goal for language models.
• Self-improvement in narrow domains is a potential development path.

Security Concerns

• Jailbreak Attacks: Exploit loopholes to bypass model restrictions (e.g., role-playing scenarios).
• Data Poisoning: Attackers can manipulate model training data to introduce backdoors.
• Prompt Injection: Injecting misleading instructions to alter model behavior.
• Security in LLMs involves a continuous cycle of attack and defense.

Conclusion

• LLMs are evolving into complex systems, comparable to operating systems in functionality.
• The field is rapidly developing with both exciting opportunities and significant security challenges.