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this paper presents a novel approach to mitigating cross-domain bias in large language models llms by employing an unlearning based method the authors propose mask language modeling MLM unlearning which selectively unlearns harmful content within the text by forgetting only toxic or biased s this technique is inspired by prior successes in unlearning privacy sensitive data and aims to preserve language modeling capabilities while reducing bias and toxicity the authors review existing debasing techniques for llms highlighting their contributions and limitations these include counterfactual data augmentation CDA sentence debas iterative nullspace projection inlp and sfdb however these methods often focus on mitigating specific types of bias and Overlook the interconnected nature of bias across domains such as gender race and religion the proposed MLM unlearning technique uses gradient Ascent to maximize the loss function for specific undesirable patterns in the training data thereby mini
