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arXiv · 2409.15747

Training Neural Networks for Modularity aids Interpretability

Satvik Golechha·Dylan Cope·Nandi Schoots
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Abstract

An approach to improve network interpretability is via clusterability, i.e., splitting a model into disjoint clusters that can be studied independently. We find pretrained models to be highly unclusterable and thus train models to be more modular using an ``enmeshment loss'' function that encourages the formation of non-interacting clusters. Using automated interpretability measures, we show that our method finds clusters that learn different, disjoint, and smaller circuits for CIFAR-10 labels. Our approach provides a promising direction for making neural networks easier to interpret.

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Satvik Golechha, Dylan Cope, Nandi Schoots. 2024-09-24. Training Neural Networks for Modularity aids Interpretability. https://arxiv.org/abs/2409.15747

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