Attribution Graphs for Dummies - 2. Building and Testing a Circuit

Mechanistic interpretability of large language models relies on tracing internal circuits to understand how models process factual queries. Analyzing a two-hop prompt regarding state capitals reveals that models often employ a combination of additive mechanisms and lookup-table-like recall. Attribution graphs visualize these pathways, highlighting how specific MLP features and attention heads contribute to output logits. However, validating these circuits through feature steering proves complex; ablating specific nodes can disrupt model performance or trigger compensatory behaviors, reflecting the model's inherent parallelism. These investigations demonstrate that factual recall is rarely a singular process, as models simultaneously maintain multiple interpretations of tokens before disambiguation. Current interpretability tools face limitations in isolating these overlapping circuits, particularly when attention patterns shift dynamically in response to interventions, necessitating more robust methods to distinguish between causal pathways and incidental correlations.