The Sequence Knowledge #724: What are the Different Types of Mechanistic Interpretability?

Discussing a taxonomy to understand the most important mechanistic interpretability methods.

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Today we will Discuss:

1. An overview of the different types of mechanistic interpretability.

2. A research paper from Texas University that details a taxonomy for mechanistic interpretability methods.

💡 AI Concept of the Day: Types of Mechanistic Interpretability

Mechanistic interpretability seeks to reverse-engineer the internal computations of machine learning models, particularly large neural networks, to understand how and why they produce specific outputs. While post-hoc interpretability methods provide correlations or approximations, mechanistic approaches aim for a causal, circuit-level understanding—analogous to reading and comprehending an algorithm’s source code. This field has matured into several distinct but interlinked types of analysis, each corresponding to a different level of granularity in the model’s internal structure.

Weight- and Parameter-Level Analysis