On the Biology of a Large Language Model - Part 1

Overview

Syllabus

• Introduction to Large Language Models
Overview of Large Language Models (LLMs)
Historical context and evolution of LLMs
Introduction to Claude 3.5 Haiku
• Understanding Neural Network Architectures
Overview of neural network fundamentals
Transformer architecture
Claude 3.5 Haiku's specific architecture
• Foundations of Circuit Tracing Methodology
Introduction to circuit tracing
Importance in understanding LLM functionality
Methodological steps involved
• Claude 3.5 Haiku's Model Internals
Examination of key components
Attention mechanisms and their role
Internal layer functionalities
• Functional Analysis of Claude 3.5 Haiku
Tracing key circuits in Claude 3.5 Haiku
Understanding context and token processing
How Claude generates coherent outputs
• Practical Circuit Tracing Techniques
Tools and software for circuit tracing
Analyzing decision-making paths in LLMs
Case studies and practical exercises
• Implications of Circuit Tracing for AI Development
Insights gained from detailed circuit analysis
Limitations and challenges of current methods
Future directions and applications in AI research
• Summary and Recap
Key takeaways from the course
Open questions in LLM research
Preparing for Part 2: Advances and Applications of LLMs

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Subjects

Computer Science