Gender Bias in Nepali-English Machine Translation: A Comparison of LLMs and Existing MT Systems

Bias in Nepali NLP is rarely addressed, as the language is classified as low-resource, which leads to the perpetuation of biases in downstream systems.Our research focuses on gender bias in Nepali-English machine translation, an area that has seen little exploration.With the emergence of Large Language Models (LLMs), there is a unique opportunity to mitigate these biases.In this study, we quantify and evaluate gender bias by constructing an occupation corpus and adapting three gender-bias challenge sets for Nepali.Our findings reveal that gender bias is prevalent in existing translation systems, with translations often reinforcing stereotypes and misrepresenting gender-specific roles.However, LLMs perform significantly better in both gender-neutral and gender-specific contexts, demonstrating less bias compared to traditional machine translation systems.Despite some quirks, LLMs offer a promising alternative for culturerich, low-resource languages like Nepali.We also explore how LLMs can improve gender accuracy and mitigate biases in occupational terms, providing a more equitable translation experience.Our work contributes to the growing effort to reduce biases in machine translation and highlights the potential of LLMs to address bias in low-resource languages, paving the way for more inclusive and accurate translation systems.