Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification

arXiv:2403.13925v1 Announce Type: cross
Abstract: 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.

abstract arxiv augmentation automated bias biases capabilities concerns cs.ai cs.cl cs.lg dataset industries language language model language models large language large language model large language models model bias novel paper quantification through type