Mitigating Large Language Model Bias: Automated Dataset Augmentation and Prejudice Quantification

Devam Mondal, Carlo Lipizzi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Despite the growing capabilities of large language models, concerns exist 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 that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics. Our code can be found on an online GitHub repository.

Original languageEnglish
Article number141
JournalComputers
Volume13
Issue number6
DOIs
StatePublished - Jun 2024

Keywords

  • computational social science
  • dataset augmentation
  • large language models
  • natural language processing

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