This chapter explores the limitations and disparities in chatbot performance across different languages, emphasizing the importance of equitable AI development. It discusses challenges faced by low-resource languages and the potential benefits of transfer learning in creating effective multilingual models. Additionally, it highlights the need for human input to refine model performance while addressing cultural biases and the predominance of English in benchmarking data.

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