Bias Reduction Techniques For LLMs In Regional Languages

• Author(s): Prem Harijan
• Paper ID: 1711858
• Page: 709-713
• Published Date: 12-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025
• DOI: https://doi.org/10.64388/IREV9I5-1711858

Abstract

Large Language Models (LLMs) are increasingly integrated into interactive systems across education, governance, healthcare, and everyday digital communication. However, because these models are trained on large-scale text corpora that reflect societal hierarchies, stereotypes, and prejudices, they often internalize and reproduce biased linguistic patterns. While a considerable body of research has investigated algorithmic bias in English-language LLMs, relatively little attention has been directed toward regional and low-resource languages, where linguistic representation is uneven and culturally specific forms of discrimination?related to caste, ethnicity, religion, gender, and socio-economic stratification?are deeply embedded in textual data. This paper proposes an advanced, multi-layered methodology for identifying, quantifying, and mitigating bias in LLMs operating in regional languages. The approach spans culturally-grounded benchmark construction, cross-lingual transfer learning, counterfactual data augmentation, adversarial representation training, and interpretability-guided conceptual editing. An evaluation framework incorporating both computational metrics and human-in-the-loop cultural assessment is presented. The study posits that bias reduction must be conceptualized as an ongoing socio-technical negotiation rather than a one-time algorithmic adjustment.

Citations

IRE Journals:
Prem Harijan "Bias Reduction Techniques For LLMs In Regional Languages" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 709-713 https://doi.org/10.64388/IREV9I5-1711858

IEEE:
Prem Harijan "Bias Reduction Techniques For LLMs In Regional Languages" Iconic Research And Engineering Journals, vol. 9, no. 5, Nov. 2025, doi: https://doi.org/10.64388/IREV9I5-1711858

APA:
Prem Harijan (2025). Bias Reduction Techniques For LLMs In Regional Languages. Iconic Research And Engineering Journals, 9(5). doi: https://doi.org/10.64388/IREV9I5-1711858

MLA:
Prem Harijan "Bias Reduction Techniques For LLMs In Regional Languages" Iconic Research And Engineering Journals, vol. 9, no. 5, Nov. 2025. Crossref, https://doi.org/10.64388/IREV9I5-1711858

BibTeX

@article{1711858,
author = {Prem Harijan},
title = {Bias Reduction Techniques For LLMs In Regional Languages},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {709-713},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1711858.pdf},
abstract = {Large Language Models (LLMs) are increasingly integrated into interactive systems across education, governance, healthcare, and everyday digital communication. However, because these models are trained on large-scale text corpora that reflect societal hierarchies, stereotypes, and prejudices, they often internalize and reproduce biased linguistic patterns. While a considerable body of research has investigated algorithmic bias in English-language LLMs, relatively little attention has been directed toward regional and low-resource languages, where linguistic representation is uneven and culturally specific forms of discrimination?related to caste, ethnicity, religion, gender, and socio-economic stratification?are deeply embedded in textual data. This paper proposes an advanced, multi-layered methodology for identifying, quantifying, and mitigating bias in LLMs operating in regional languages. The approach spans culturally-grounded benchmark construction, cross-lingual transfer learning, counterfactual data augmentation, adversarial representation training, and interpretability-guided conceptual editing. An evaluation framework incorporating both computational metrics and human-in-the-loop cultural assessment is presented. The study posits that bias reduction must be conceptualized as an ongoing socio-technical negotiation rather than a one-time algorithmic adjustment.},
month = {November}
}