Home / Current Issue / Paper 1711858
Bias Reduction Techniques For LLMs In Regional Languages
Subject area: Science,Engineering and Technology · Area of research: Computer Science and Engineering
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.
References
[1] References (selected — sources used to build the article)
[2] Gallegos IO et al., “Bias and Fairness in Large Language Models: A Survey.” (survey of bias evaluation & mitigation).
[3] Nangia N., et al., “CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models.” EMNLP 2020.
[4] Lauscher et al., “XWEAT / Multilingual analysis of biases in embeddings.” (multilingual WEAT work). Reusens et al., “Cross-Lingual Transfer of Debiasing Techniques.” EMNLP 2023 (transferability evidence). Balashankar et al., “Active generation & Counterfactual Data Augmentation” (Findings EMNLP 2023).
[5] Li Y., “Contrastive Self-Debiasing Model with Double Data Augmentation (CD³).” 2024.
[6] Recent reviews and papers on bias in LLMs and multilingual bias (2024–2025), including domain reviews and language-specific benchmark adaptations.
[7] News/technical note: “Robustly Improving LLM Fairness … affine concept editing” (study reporting interpretability-guided editing reducing bias).
How to cite this paper
@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},
doi = {https://doi.org/10.64388/IREV9I5-1711858}
}