Mechanistic Interpretability: Peeking Inside an LLM

Mechanistic interpretability research allows scientists to examine the inner workings of large language models by analyzing neural activations, attention patterns, and information flow through the network's layers. Researchers have discovered that LLMs develop internal world models, can be steered through activation manipulation, and possess latent knowledge not reflected in their outputs, with applications ranging from improving model safety to reducing hallucinations.