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arXiv · 2404.03147

Eigenpruning: an Interpretability-Inspired PEFT Method

Tomás Vergara-Browne·Álvaro Soto·Akiko Aizawa
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Abstract

We introduce eigenpruning, a method that removes singular values from weight matrices in an LLM to improve its performance in a particular task. This method is inspired by interpretability methods designed to automatically find subnetworks of a model which solve a specific task. In our tests, the pruned model outperforms the original model by a large margin, while only requiring minimal computation to prune the weight matrices. In the case of a small synthetic task in integer multiplication, the Phi-2 model can improve its accuracy in the test set from 13.75% to 97.50%. Interestingly, these results seem to indicate the existence of a computation path that can solve the task very effectively, but it was not being used by the original model. Finally, we publicly release our implementation.

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Tomás Vergara-Browne, Álvaro Soto, Akiko Aizawa. 2024-06-20. Eigenpruning: an Interpretability-Inspired PEFT Method. https://arxiv.org/abs/2404.03147

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