Bias Reduction Techniques For LLMs In Regional Languages

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.