• Inequality. LLMs are recognized to exhibit reduced efficacy when operating with low-resource languages or certain dialects like African American vernacular English . As a result, users representing these linguistic variations may encounter sub-optimal performance compared to native English speakers. This diminished user experience can manifest in various ways, potentially leading to outputs of inferior quality, increased biases, and even model-generated hallucinations.