Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias Mitigation
<|reference_start|>Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias Mitigation: Large language models (LLMs) often inherit biases from vast amounts of training corpora. Traditional debiasing methods, while effective to some extent, do not completely eliminate memorized biases and toxicity in LLMs. In this paper, we study an unlearning-based approach to debiasing in LLMs by performing gradient ascent on hate speech against minority groups, i.e., minimizing the likelihood of biased or toxic content. Specifically, we propose a mask language modeling unlearning technique, which unlearns the harmful part of the text. This method enables LLMs to selectively forget and disassociate from biased and harmful content. Experimental results demonstrate the effectiveness of our approach in diminishing bias while maintaining the language modeling abilities. Surprisingly, the results also unveil an unexpected potential for cross-domain transfer unlearning: debiasing in one bias form (e.g. gender) may contribute to mitigating others (e.g. race and religion).<|reference_end|>
@article{lu2024towards,
    title={Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias
  Mitigation},
    author={Huimin Lu, Masaru Isonuma, Junichiro Mori, Ichiro Sakata},
    journal={arXiv preprint arXiv:2407.16951},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16951},
    primaryClass={cs.CL cs.LG}
}