Mechanistic Interpretability

This section explores the critical field of AI interpretability, focusing on understanding how modern deep learning models, particularly Large Language Models (LLMs), make decisions and process information. The content examines three essential aspects of mechanistic interpretability: the nature of emergent features in neural networks, practical tools for visualizing model internals, and the architectural evolution from recurrent networks to attention-based models. These topics collectively provide insights into the black box of AI systems, revealing how complex behaviors emerge from mathematical transformations and learned representations distributed across network layers.

The exploration begins with the fundamental concept of features in LLMs, where emergent representations arise from training rather than explicit engineering, encoding abstract concepts through patterns of neuron activations across transformer layers. Moving from theory to practice, the investigation employs Neuroscope, a visualization tool that reveals how GPT-J-6B’s 6 billion parameters organize into distinct activation patterns, showing a clear progression from sparse, distributed representations in early layers to coherent, unified activations in deeper layers that enable complex reasoning and sequence prediction. Finally, the historical perspective traces the evolution from RNN-based sequence-to-sequence models with their attention mechanisms to the revolutionary Transformer architecture, which abandons recurrence entirely in favor of self-attention, enabling parallel processing and superior capture of long-range dependencies that form the foundation of modern language models like BERT and GPT variants.