Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models
Circuit Identification;Modularity;Continuous Sparsification
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In this work, we study the modularity of neural networks by analyzing circuits for highly compositional subtasks within a transformer-based language model.
@misc{
mondorf2024circuit,
title={Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models},
author={Philipp Mondorf and Sondre Wold and Barbara Plank},
year={2024},
url={https://openreview.net/forum?id=u4XyECA6Zd}
}
A fundamental question in interpretability research is to what extent neural networks, particularly language models, implement reusable functions via subnetworks that can be composed to perform more complex tasks. Recent developments in mechanistic interpretability have made progress in identifying subnetworks, often referred to as circuits, which represent the minimal computational subgraph 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 relate to each other. To address this gap, we examine the modularity of neural networks by analyzing circuits for highly compositional subtasks within a transformer-based language model. Specifically, given a probabilistic context-free grammar, we identify and compare circuits responsible for ten modular string-edit operations. Our results indicate that functionally similar circuits exhibit both notable node overlap and cross-task faithfulness. Moreover, we demonstrate that the circuits identified can be reused and combined through subnetwork set operations to represent more complex functional capabilities of the model.
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~Philipp_Mondorf1 ~Sondre_Wold1 ~Barbara_Plank2
Philipp Mondorf Sondre Wold Barbara Plank
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Circuit Identification Modularity Continuous Sparsification
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mondorf|circuit_compositions_exploring_modular_structures_in_transformerbased_language_models
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interpretability and explainable AI
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Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission