Overview

Description

Interpretability in NNs refers to the model's ability to understand and explain how the model processes inputs and generates outputs.

Varieties:

• Attention visualization techniques
• Probing methods
• Explaining LLM predictions with attribution methods
• Interpretability in transformer-based LLMs
• Mechanistic interpretability
• Trade-offs between interpretability and performance