Gender Bias in Machine Translation and The Era of Large Language Models

2024/01/18 by Eva Vanmassenhove, Vanmassenhove, Eva · 1 voice · 1 citation
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Text Readability and Simplification #cs.AI #cs.CL #cs.CY

paper · pdf · doi:10.48550/arxiv.2401.10016

openalex publication_date 2024/01/18 · arxiv published 2024/01/18 · arxiv updated 2024/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This chapter examines the role of Machine Translation in perpetuating gender bias, highlighting the challenges posed by cross-linguistic settings and statistical dependencies. A comprehensive overview of relevant existing work related to gender bias in both conventional Neural Machine Translation approaches and Generative Pretrained Transformer models employed as Machine Translation systems is provided. Through an experiment using ChatGPT (based on GPT-3.5) in an English-Italian translation context, we further assess ChatGPT's current capacity to address gender bias. The findings emphasize the ongoing need for advancements in mitigating bias in Machine Translation systems and underscore the importance of fostering fairness and inclusivity in language technologies.

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Discussions

• Study Reveals Gender Bias in ChatGPT Translations [hn, 2 points, 0 comments]

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