Mechanistic Interpretation

Mechanistic interpretability aims to understand how neural networks arrive at their outputs by analyzing their internal workings, moving beyond simply assessing their accuracy. Current research focuses on identifying and characterizing the specific internal mechanisms (e.g., attention heads, neural circuits) responsible for model behavior in various tasks, using techniques like circuit discovery and causal mediation analysis, and applying these methods to transformer-based models and recurrent neural networks. This work is crucial for improving model reliability, debugging errors, and gaining insights into the cognitive processes these models may be emulating, ultimately advancing both artificial intelligence and our understanding of complex systems.