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

arXiv: https://arxiv.org/abs/2403.13925 · HTML: https://mlsanity.com/paper/2403.13925/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets](https://mlsanity.com/paper/1908.07898/) (2019)
- [Reducing Sentiment Bias in Language Models via Counterfactual Evaluation](https://mlsanity.com/paper/1911.03064/) (2019)
- [Examining the Inductive Bias of Neural Language Models with Artificial Languages](https://mlsanity.com/paper/2106.01044/) (2021)
- [A Trip Towards Fairness: Bias and De-Biasing in Large Language Models](https://mlsanity.com/paper/2305.13862/) (2023)
- [KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application](https://mlsanity.com/paper/2305.17701/) (2023)
- [Optimizing National Security Strategies through LLM-Driven Artificial Intelligence Integration](https://mlsanity.com/paper/2305.13927/) (2023)

## Abstract (arXiv)

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.