Intro to Large Language Models

Overview

• Recently gave a 30-minute talk on large language models (LLMs).
• Re-recording the talk for YouTube due to positive feedback from attendees.

What are Large Language Models?

• LLMs can be simplified to two files:
  • Parameters File: Contains the weights of the neural network.
  • Run Code File: Contains the code to execute the model, can be in various programming languages (e.g., C, Python).
• Example: Llama 270B Model by Meta AI.
  • Part of the Llama 2 series with models ranging from 7B to 70B parameters.
  • 70B parameters = 140GB file size (2 bytes per parameter).
• Allows for running the model on personal devices without internet access.
• Can generate text based on prompts.

Model Training vs Inference

• Model Inference: Running the model with available parameters (simple process).
• Model Training: More complex; involves:
  • Collecting ~10 terabytes of text from the internet (web crawl).
  • Training on a GPU cluster (6,000 GPUs over 12 days).
  • Cost ~ $2 million.
  • Training can be viewed as compressing text data into parameters (like lossy compression).

Neural Network Functionality

• LLMs primarily predict the next word in a sequence.
• Example: Given text, predict next most likely word.
• Relationships exist between prediction and compression.
• LLMs learn vast information about the world through next-word prediction.

Using Trained Neural Networks

• Generate outputs by feeding in text and obtaining the next word.
• Results are often creative outputs (e.g., poetry), but may include inaccuracies or hallucinations.

Transformer Neural Network Architecture

• Understand the architecture and mathematical operations involved.
• 100B+ parameters in LLMs are dispersed throughout.
• Key understanding: We can optimize parameters for better predictions but don't fully understand their functions.

Stages of LLM Development

Stage 1: Pre-Training

• Involves training on vast internet text (lower quality).
• Requires significant computational resources.
• Objective is knowledge accumulation.

Stage 2: Fine-Tuning

• Shift to high-quality Q&A document training (e.g., human-assisted).
• Used for creating assistant models that better respond to questions.

Stage 3: Reinforcement Learning from Human Feedback

• Optional stage to further refine the model based on comparisons of generated responses.
• Aims to improve output quality through iterative feedback.

Leading Models and Performance

• Proprietary models (e.g., GPT series) outperform open-source models but lack full accessibility.
• Ongoing competition between proprietary and open-source models.

Future Directions

Scaling Laws

• Performance in LLMs is largely predictable based on parameters and training data.
• Larger models with more data tend to perform better.

Tool Usage

• LLMs evolve to use tools (e.g., browsing, calculators) to enhance problem-solving abilities.
• Example of organizing data, performing calculations, or generating visuals through prompts.

Multimodality

• Ability to process and generate images, audio, and text.
• Potential for speech-to-speech communication capabilities.

System One vs. System Two Thinking

• Current LLMs function similarly to instinctive thinking (System One).
• Future work aims to introduce reflective problem-solving (System Two).

Self-Improvement and Customization

• Potential for models to improve beyond human imitation.
• Discussion of methods for tailoring models to specific tasks or functions.

Security Challenges

Types of Attacks

• Jailbreak Attacks: Bypass safety mechanisms (e.g., roleplay scenarios).
• Prompt Injection Attacks: Hijack instructions through subtle prompts.
• Data Poisoning: Introduce harmful operating conditions via training data.

Defense Mechanisms

• Security measures in place to mitigate various attacks, with ongoing research in this area.

Conclusion

• LLMs offer a new computing paradigm with vast potential but also face security challenges.
• Continuous evolution in model capabilities and security measures is critical for future applications.