Chapter 3: Interpretability

Introduction to Interpretability

Neural networks, particularly large language models, have demonstrated impressive capabilities but often function as "black boxes." Interpretability research aims to understand how these models work internally and why they make specific predictions or decisions.

This chapter introduces the field of neural network interpretability, with a focus on mechanistic interpretability—the approach that seeks to understand the internal mechanisms and computations within these models.

Motivation and Importance

Understanding how neural networks function is important for several reasons:

• Safety and Alignment: To ensure AI systems behave as intended, especially as they become more capable
• Debugging: To identify and fix issues in model behavior
• Trust: To build justified confidence in model outputs and capabilities
• Scientific Understanding: To advance our knowledge of how artificial intelligence works
• Capability Improvement: To enable targeted improvements based on mechanistic insights

Approaches to Interpretability

The field of interpretability can be roughly divided into two approaches:

Post-hoc Interpretability

Post-hoc interpretability attempts to explain already-trained models by analyzing their behavior, often through methods like:

• Feature visualization
• Attribution methods (e.g., LIME, SHAP)
• Concept-based explanations
• Surrogate models

Mechanistic Interpretability

Mechanistic interpretability attempts to reverse-engineer the inner workings of neural networks by:

• Identifying specific computational circuits within the network
• Understanding how these circuits implement particular functions
• Mapping information flow through the network
• Relating network components to human-interpretable concepts

Tools and Techniques

Several tools and techniques are commonly used in mechanistic interpretability research:

Activation Analysis

Studying the activation patterns of neurons in response to different inputs to understand what information they encode.

Causal Intervention

Modifying activations or weights to observe the effect on model behavior, helping to establish causal relationships.

Weight Analysis

Examining learned weights to understand the connections between components of the network.

Library Tools

Libraries like TransformerLens provide specialized tools for interpretability research on transformer models.

Challenges and Limitations

Interpretability research faces several challenges:

• Scale: Modern models contain billions of parameters, making comprehensive analysis difficult
• Complexity: Neural networks implement distributed representations that don't always align with human concepts
• Verification: It's often difficult to verify that an interpretation is correct
• Generalizability: Insights from one model or task may not generalize to others

Conclusion

Interpretability, particularly mechanistic interpretability, is a growing field that aims to demystify neural networks. While challenging, this work is essential for building safe, trustworthy, and well-understood AI systems. The subsequent chapters will explore specific techniques and case studies in mechanistic interpretability.