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Bias Reduction Techniques For LLMs In Regional Languages
Subject area: Science,Engineering and Technology · Area of research: Computer Science and Engineering
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] Gallegos IO et al., “Bias and Fairness in Large Language Models: A Survey.” (survey of bias evaluation & mitigation).
[2] Nangia N., et al., “CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models.” EMNLP 2020.
[3] 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).
[4] Li Y., “Contrastive Self-Debiasing Model with Double Data Augmentation (CD³).” 2024.
[5] Recent reviews and papers on bias in LLMs and multilingual bias (2024–2025), including domain reviews and language-specific benchmark adaptations.
[6] 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}
}