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Large Language Models (LLMs) are trained on a predominantly Western corpus, leading to cultural biases that can hinder their effectiveness and adoption in non-Western contexts. To address this, LLMs must be adapted to regional nuances, incorporating local languages, idioms, and cultural references. This involves fine-tuning models on region-specific datasets, integrating behavioral insights, and establishing governance frameworks to ensure ethical and safe AI use.

Large Language Models (LLMs) are often trained on data that reflects Western perspectives, leading to biases and cultural insensitivity. To address this, a three-layered approach is proposed: culturally curated data, behavioral insights, and region-specific governance models. This approach aims to create AI that understands and respects local cultures, values, and communication styles, ensuring global adoption and avoiding cultural erasure.

Decolonizing AI: Adapting LLMs for Global Cultures