Dissecting Fine-Tuning Unlearning in Large Language Models
<|reference_start|>Dissecting Fine-Tuning Unlearning in Large Language Models: Fine-tuning-based unlearning methods prevail for preventing targeted harmful, sensitive, or copyrighted information within large language models while preserving overall capabilities. However, the true effectiveness of these methods is unclear. In this paper, we delve into the limitations of fine-tuning-based unlearning through activation patching and parameter restoration experiments. Our findings reveal that these methods alter the model's knowledge retrieval process, rather than genuinely erasing the problematic knowledge embedded in the model parameters. Furthermore, behavioral tests demonstrate that the unlearning mechanisms inevitably impact the global behavior of the models, affecting unrelated knowledge or capabilities. Our work advocates the development of more resilient unlearning techniques for truly erasing knowledge. Our code is released at https://github.com/yihuaihong/Dissecting-FT-Unlearning.<|reference_end|>
@article{hong2024dissecting,
    title={Dissecting Fine-Tuning Unlearning in Large Language Models},
    author={Yihuai Hong, Yuelin Zou, Lijie Hu, Ziqian Zeng, Di Wang, Haiqin Yang},
    journal={arXiv preprint arXiv:2410.06606},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06606},
    primaryClass={cs.CL cs.LG}
}