Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification
<|reference_start|>Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification: Despite the growing capabilities of large language models, there exists concerns about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers and in the context of 'restricted industries' with limited data. We additionally create two new additional metrics, the mb-index and db-index, to quantify bias, considering the idea that bias occurs due to both intrinsic model architecture and dataset.<|reference_end|>
@article{mondal2024reducing,
    title={Reducing Large Language Model Bias with Emphasis on 'Restricted
  Industries': Automated Dataset Augmentation and Prejudice Quantification},
    author={Devam Mondal, Carlo Lipizzi},
    journal={arXiv preprint arXiv:2403.13925},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13925},
    primaryClass={cs.CL cs.AI cs.LG}
}