Dissecting Language Models: Machine Unlearning via Selective Pruning
<|reference_start|>Dissecting Language Models: Machine Unlearning via Selective Pruning: Understanding and shaping the behaviour of Large Language Models (LLMs) is increasingly important as applications become more powerful and more frequently adopted. This paper introduces a machine unlearning method specifically designed for LLMs. We introduce a selective pruning method for LLMs that removes neurons based on their relative importance on a targeted capability compared to overall network performance. This approach is a compute- and data-efficient method for identifying and removing neurons that enable specific behaviours. Our findings reveal that both feed-forward and attention neurons in LLMs are specialized; that is, for specific tasks, certain neurons are more crucial than others. Code from all experiments is available at https://github.com/nickypro/selective-pruning<|reference_end|>
@article{pochinkov2024dissecting,
    title={Dissecting Language Models: Machine Unlearning via Selective Pruning},
    author={Nicholas Pochinkov and Nandi Schoots},
    journal={arXiv preprint arXiv:2403.01267},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01267},
    primaryClass={cs.LG cs.CL}
}