Gender Bias in N epali- E nglish Machine Translation: A Comparison of LLM s and Existing MT Systems (2025)

Abstract: 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(LLM), 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 culture-rich, 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.

## introduction based on @xcite , nepali is a grammatical gender language, unlike english, which is a notional gender language. in nepali, verbs and adjectives carry gender inflections, while pronouns indicate formality, affecting the verb form. for example, "he/she is tall" translates to उनी अग्ली छन् (oo-ni uglee chhinn) for females and उनी अग्ला छन् (oo-ni uglaa chhann) for males in a famil-* work done while at diyo.ai iar setting. the pronoun changes for different levels of formality, altering the verb and adjective accordingly. the most formal third-person pronoun, उहाँ (oo-haan), uses a gender-neutral verb, while other pronouns use gendered verbs. there have been extensive studies on gender bias in translation for grammatical gender languages @xcite @xcite , but nepali remains unexplored. due to nepali's low-resource status @xcite , the focus has traditionally been on improving translation accuracy, often neglecting issues of bias. this can result in fluent yet biased outputs, reinforcing stereotypes and prejudices over time @xcite . we define "bias" as the systematic and unfair representation of one gender over another in translation outputs. in this study, we consider only two genders: male and female. the inclusion of other genders is beyond the scope of this work. our experiments identify bias in three ways: reinforcement of gender stereotypes, incorrect gender assignments to neutral and opposite-gendered terms, and unequal translation accuracy across genders. as highlighted by @xcite , these biases can cause significant harm, particularly by reinforcing stereotypes. in nepali-english translation, this is evident in how systems often associate occupations with specific genders, use respectful pronouns predominantly for men, and fail to properly represent women in high-ranking positions. our work aims to study and evaluate these biases in nepali-english machine translation. our major contributions are: data and code are publicly available. foot_0 ## conclusion in conclusion, we assessed gender bias in nepali-english machine translation in existing mt systems and llms. we developed a nepali-specific occupation corpus and adapted three challenge sets for a gender-neutral and two gender-specific contexts. our findings show that traditional mt systems reinforce stereotypes, while llms reduce bias but often neutralize gender distinctions. as llms continue to evolve, incorporating context-aware prompting and multi-gender translation strategies could help strike a balance between gender neutrality and accurate representation. by refining both mt and llm strategies, we can develop fairer translation systems for low-resource languages like nepali.