Mechanistic Interpretability: Peeking Inside an LLM

Mechanistic interpretability seeks to decipher the inner workings of a large language model's neural network to understand how it processes information and reaches its conclusions. Researchers probe the model by observing key components like attention heads and the "residual stream," which is the data flowing through the network's layers. This analysis can involve actively intervening in the model's process through methods like ablation, where parts of the network are disabled, or steering, where activations are modified to guide the output. Understanding these internal mechanisms is crucial for improving model performance, ensuring safety, explaining AI decisions, and even gaining new scientific insights into language and cognition.