Generative AI’s effectiveness hinges on its training data, which often lacks diversity in language representation. Currently, most AI models are predominantly trained on English and a few major languages, limiting their ability to understand and generate content in less common languages. This language bias can lead to significant knowledge gaps in underrepresented linguistic communities, ultimately affecting equality and inclusivity in technology. Expanding generative AI training to include a broader range of languages can enhance its accuracy, relevance, and accessibility. It will empower diverse cultures by allowing individuals to engage with AI in their native languages, fostering creativity and innovation. Moreover, multilingual training can improve the robustness of AI applications in global markets. Strengthening AI’s language capabilities is crucial for advancing its role as a tool for global communication, education, and problem-solving. Addressing this linguistic disparity is essential for creating a more equitable digital landscape.
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