Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for
  Large Language Models
Michele Mastromattei Fabio Massimo Zanzotto
Neural network pruning has become increasingly crucial due to the complexity
of neural network models and their widespread use in various fields. Existing
pruning algorithms often suffer from limitations such as architecture
specificity, excessive complexity and reliance on complex calculations,
rendering them impractical for real-world applications. In this paper, we
propose KEN: a straightforward, universal and unstructured pruning algorithm
based on Kernel Density Estimation (KDE). KEN aims to construct optimized
transformer models by selectively preserving the most significant parameters
while restoring others to their pre-training state. This approach maintains
model performance while allowing storage of only the optimized subnetwork,
leading to significant memory savings. Extensive evaluations on seven
transformer models demonstrate that KEN achieves equal or better performance
than the original models with a minimum parameter reduction of 25%. In-depth
comparisons against other pruning and PEFT algorithms confirm KEN
effectiveness. Furthermore, we introduce KEN_viz, an explainable tool that
visualizes the optimized model composition and the subnetwork selected by KEN.