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Gender Bias in Machine Translation and The Era of Large Language Models (2401.10016v1)

Published 18 Jan 2024 in cs.CL, cs.AI, and cs.CY

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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Authors (1)
  1. Eva Vanmassenhove (13 papers)
Citations (1)
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