Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models

Understand

A fundamental question in interpretability research is to what extent neural networks, particularly language models, implement reusable functions through subnetworks that can be composed to perform more complex tasks.

• Recent advances in mechanistic interpretability have made progress in identifying $\textit{circuits}$, which represent the minimal computational subgraphs responsible for a model's behavior on specific tasks.
• However, most studies focus on identifying circuits for individual tasks without investigating how functionally similar circuits $\textit{relate}$ to each other.
• To address this gap, we study the modularity of neural networks by analyzing circuits for highly compositional subtasks within a transformer-based language model.