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

TL;DR

This paper explores gender bias in machine translation, especially in large language models like ChatGPT, highlighting challenges and the need for improved fairness and inclusivity in language technologies.

Contribution

It provides a comprehensive overview of gender bias issues in both traditional and large language model-based machine translation systems, including experimental assessment of ChatGPT.

Findings

ChatGPT exhibits gender bias in English-Italian translation

Current models still require significant improvements to mitigate bias

Addressing bias is crucial for fair and inclusive language technology development

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.