🤖 AI Summary
This study challenges the prevailing assumption that larger language models are inherently less interpretable, investigating whether interpretability necessarily degrades with increasing parameter count. Employing mechanistic interpretability methods and circuit localization techniques, the authors systematically analyze three key circuit types across the Pythia and Qwen2.5 model families, comparing the effects of standard multi-head attention versus grouped-query attention (GQA) on circuit concentration and mechanistic stability. The findings reveal that GQA significantly enhances both the concentration of circuits and their stability across scales, while the fact-recall circuits in Qwen2.5 exhibit a discrete phase transition at a critical model size. These results suggest that attention architecture exerts a stronger influence on interpretability than model scale alone, offering a promising direction for designing large models with high interpretability.
📝 Abstract
Mechanistic interpretability assumes that circuit analysis becomes harder as models scale. We challenge this assumption by showing that the attention architecture matters more than parameter count. Studying three circuit types across Pythia and Qwen2.5, we find that grouped query attention produces circuits that are far more concentrated and mechanistically stable than standard multi-head attention at comparable scales. The same concentration pattern holds across indirect object identification, induction heads, and factual recall. Within a single architecture family (Qwen2.5), factual recall circuits undergo a discrete phase transition above a critical scale, collapsing to a single bottleneck rather than degrading gradually. These findings suggest that some architectural choices make large models more tractable to study and that interpretability difficulty is not a fixed consequence of model size.