Introduction to Large Language Models (LLMs)

Objective

• Provide an intuitive understanding of large language models, such as ChatGPT.
• Discuss the strengths, weaknesses, and intricacies of LLMs.

Key Questions Addressed

• What are LLMs?
• How are they constructed?
• What are the psychological implications of using such tools?

Structure of the Lecture

1. Building LLMs
2. Pre-training
3. Tokenization
4. Neural Network Training
5. Inference
6. Post-Training
7. Cognitive Implications
8. Reinforcement Learning
9. Future Directions

Building Large Language Models

Pre-training Stage

• Objective: Acquire a vast amount of knowledge by processing internet data.
• Data Collection: Use datasets like the Pile or Common Crawl to gather diverse, high-quality documents.
• Data Size: Typical datasets might be around 44TB, but filtered down to about 15 trillion tokens.

Tokenization

• Convert text into a sequence of symbols (tokens).
• Use byte pair encoding to compress data while maintaining meaning.
• Example: Convert raw text into a sequence of tokens using a tokenizer tool.

Neural Network Training

• Objective: Model statistical relationships in text data.
• Process: Sliding window of tokens fed into a neural network to predict subsequent tokens.
• Adjustments: Neural networks are updated based on prediction accuracy.

Inference

• Objective: Generate new data from the model.
• Process: Start with a series of tokens, predict the next token using probability distributions.

Post-Training

Fine-Tuning

• Refine models for specific tasks using human-generated conversation datasets.
• Human Labelers: Essential in creating frameworks for ideal assistant responses.

Tool Use and Enhancements

• Encourage models to use tools (e.g., web search) to mitigate limitations, such as hallucinations.

Cognitive Implications

• Hallucinations: Models may invent information when uncertain.
• Tokenization Limits: Spelling and counting tasks may fail due to tokenization quirks.
• Encourage distributed thinking across tokens.

Reinforcement Learning (RL)

Motivation

• Enable models to discover solutions through trial and error, akin to practicing problems.

RL Process

• Generate multiple solutions to a problem and reinforce successful ones.
• Reward models learn to simulate human judgments.

Emergent Behavior

• Models develop "chains of thought," improving problem-solving abilities.

Future Directions

Multimodality

• Integration of audio and images with text processing.

Extended Task Management

• Development of AI agents capable of handling complex, long-term tasks.

Embedded Systems

• More seamless integration in daily tools and environments.

Continuous Learning

• Potential for models to learn and adapt during inference stages.

Finding LLMs

• Use tools like ChatGPT, Google Gemini, DeepSeek R1 to explore different models and capabilities.

Conclusion

• LLMs present immense potential but require careful handling due to their inherent limitations.
• Use them as tools for augmentation, not replacement.

Recommended Resources

• ElMira Leaderboard: Tracks top-performing models.
• AI News Newsletter: Keeps updated on AI developments.
• Together.AI and LM Studio: Platforms for exploring and running models locally.