Mechanistic Interpretability

Mechanistic interpretability aims to understand how neural networks, particularly large language models (LLMs) and image models, perform computations by reverse-engineering their internal mechanisms. Current research focuses on identifying and characterizing "circuits"—minimal subnetworks responsible for specific tasks—within transformer architectures, often using techniques like sparse autoencoders and activation patching to analyze neuron activations and attention mechanisms. This work is crucial for improving model reliability, safety, and trustworthiness, as well as for gaining fundamental insights into the nature of artificial intelligence and its relationship to human cognition.